WO2021217853A1 - 损伤图像智能定损方法、装置、电子设备及存储介质 - Google Patents

损伤图像智能定损方法、装置、电子设备及存储介质 Download PDF

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Publication number
WO2021217853A1
WO2021217853A1 PCT/CN2020/098972 CN2020098972W WO2021217853A1 WO 2021217853 A1 WO2021217853 A1 WO 2021217853A1 CN 2020098972 W CN2020098972 W CN 2020098972W WO 2021217853 A1 WO2021217853 A1 WO 2021217853A1
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Prior art keywords
damage
image set
image
value
standard
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PCT/CN2020/098972
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English (en)
French (fr)
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赵霄鸿
刘莉红
刘玉宇
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平安科技（深圳）有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30248—Vehicle exterior or interior
    • G06T2207/30252—Vehicle exterior; Vicinity of vehicle

Definitions

  • This application relates to the field of artificial intelligence technology, and in particular to an intelligent damage assessment method, device, electronic equipment and computer-readable storage medium for damage images.
  • the damaged image refers to an image taken of a damaged target, for example, an accident vehicle image taken in a vehicle accident, a road image taken when a road collapses, and the like. Damage images are generally used to determine damage after the event. For example, an insurance company determines the damage of a vehicle in an accident based on the damage image for insurance compensation.
  • the inventor realizes that since damage images are usually similar to real damage images but are not real damage images, for example, for vehicle damage images, soil and stains contained in vehicle parts are often regarded as image damage.
  • the random method is mainly used to obtain the real damage image in the damage image, but the random method has unstable characteristics, which easily affects the acquisition probability of the real damage image in the damage image, and thus affects the damage assessment efficiency of the damage image.
  • the damage assessment of the damaged image usually requires the identification of the damaged image to determine the damaged location of the target, and the accuracy of the damaged location recognition mainly depends on the model for recognizing the damaged image.
  • the current model used in the industry mainly collects the appearance data of various images in advance for learning, and then uses the constructed image location damage recognition model to identify the damage location in the damaged image.
  • the recognition accuracy it is usually necessary to obtain as much appearance image data of various targets as sample images for training, and the training and parameter optimization process period of the model algorithm is usually long, and the overall implementation cost is relatively high. Therefore, in the processing of damage image damage assessment, a damage assessment solution that efficiently recognizes the damage location of the image is also needed.
  • the present application provides a method, device, electronic device, and computer-readable storage medium for intelligent damage assessment of damaged images, the main purpose of which is to improve the damage assessment efficiency of damaged images and the detection efficiency of damaged locations.
  • an intelligent damage assessment method for damaged images includes:
  • the damage degree detection is performed on the target damage image set through the pre-trained image damage degree detection model to obtain the damage degree of the target damage image set.
  • an electronic device which includes:
  • At least one processor and,
  • a memory communicatively connected with the at least one processor; wherein,
  • the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the following intelligent damage assessment method for damaged images:
  • the damage degree detection is performed on the target damage image set through the pre-trained image damage degree detection model to obtain the damage degree of the target damage image set.
  • this application also provides a computer-readable storage medium, including a storage data area and a storage program area.
  • the storage data area stores data created according to the use of blockchain nodes
  • the storage program area stores computer programs.
  • the damage degree detection is performed on the target damage image set through the pre-trained image damage degree detection model to obtain the damage degree of the target damage image set.
  • an intelligent damage assessment device for damaged images which includes:
  • the sampling module is used to obtain an original damage image set, and sample the original damage image set according to the historical damage image set to obtain a sampled image set;
  • An adjustment module configured to obtain all feature layers of each sampled image in the sampled image set, adjust the size of each feature layer in the sampled image to the same size, to obtain an initial damage image set;
  • the enhancement module is used to calculate the semantic information of each feature layer in the initial damage image set, and is used to perform feature enhancement on the semantic information using a preset semantic feature enhancement function to obtain a standard damage image set;
  • the detection module is used to calculate the damage value of each characteristic layer in the standard damage image set, detect the damage position of the standard damage image set according to the damage value, and cut out the damage position in the standard damage image set corresponding to the damage position. Image, get the target damage image set;
  • the damage assessment module is used to detect the damage degree of the target damage image set through the pre-trained image damage degree detection model to obtain the damage degree of the target damage image set.
  • the embodiment of the application first samples the original damage image set to obtain the sampled image set, which can filter out the rare images in the original damage image set, and increases the probability of obtaining true damage images in the original damage image set, thereby increasing the original damage image set
  • the embodiment of the application adjusts the size of each feature layer in the sampled image to the same size to obtain an initial damage image set, and calculates the semantic information of each feature layer in the initial damage image set, And combining the preset semantic feature enhancement function to perform feature enhancement on the semantic information to obtain a standard damage image set, which realizes the equalization of the semantic information of all feature layers in the sampled image; further, the embodiment of the present application adopts each feature
  • the damage value of the layer detects the damage position of the standard damage image set, and there is no need to construct and train the image position damage recognition model, so that the detection efficiency of the damage position can be improved; in addition, the standard corresponding to the damage position is cut out in the embodiment of the application.
  • the target damage image set is obtained from the images in the damage image set, and the damage degree detection is performed on the target damage image set through the pre-trained image damage degree detection model. That is, the embodiment of the present application only uses the image damage degree detection model to perform the damage position The detection of the damage degree ensures that there will be no deviation in the image loss due to artificial subjective reasons, and further improves the calculation efficiency. Therefore, the method, device, electronic device, and computer-readable storage medium for intelligent damage assessment of damaged images proposed in the embodiments of the present application can improve the efficiency of damage assessment and the detection efficiency of damaged locations.
  • FIG. 1 is a schematic flowchart of an intelligent damage assessment method for damage images according to an embodiment of the application
  • FIG. 2 is a schematic diagram of a detailed implementation process of step S5 in the intelligent damage assessment method for damage images in FIG. 1 of this application;
  • FIG. 3 is a schematic diagram of modules of an intelligent damage assessment device for damage images provided by an embodiment of the application.
  • FIG. 4 is a schematic diagram of the internal structure of an electronic device that implements an intelligent damage assessment method for damage images according to an embodiment of the application;
  • the execution subject of the method for intelligent damage assessment of damaged images provided in the embodiments of the present application includes, but is not limited to, at least one of the electronic devices that can be configured to execute the method provided in the embodiments of the present application, such as a server and a terminal.
  • the intelligent damage assessment method for damaged images can be executed by software or hardware installed on terminal devices or server devices, and the software can be a blockchain platform.
  • the server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.
  • Blockchain is a new application mode of computer technology such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm.
  • Blockchain essentially a decentralized database, is a series of data blocks associated with cryptographic methods. Each data block contains a batch of network transaction information for verification. The validity of the information (anti-counterfeiting) and the generation of the next block.
  • the blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.
  • the underlying platform of the blockchain can include processing modules such as user management, basic services, smart contracts, and operation monitoring.
  • the user management module is responsible for the identity information management of all blockchain participants, including the maintenance of public and private key generation (account management), key management, and maintenance of the correspondence between the user’s real identity and the blockchain address (authority management), etc.
  • authorization supervise and audit certain real-identity transactions, and provide risk control rule configuration (risk control audit); basic service modules are deployed on all blockchain node devices to verify the validity of business requests, After completing the consensus on the valid request, it is recorded on the storage.
  • the basic service For a new business request, the basic service first performs interface adaptation analysis and authentication processing (interface adaptation), and then encrypts the business information through the consensus algorithm (consensus management), After encryption, it is completely and consistently transmitted to the shared ledger (network communication), and recorded and stored; the smart contract module is responsible for contract registration and issuance, contract triggering and contract execution.
  • interface adaptation interface adaptation
  • consensus algorithm consensus algorithm
  • the smart contract module is responsible for contract registration and issuance, contract triggering and contract execution.
  • the operation monitoring module is mainly responsible for the deployment of the product release process , Configuration modification, contract settings, cloud adaptation, and visual output of real-time status during product operation, such as: alarms, monitoring network conditions, monitoring node equipment health status, etc.
  • This application provides a method for intelligent damage assessment of damaged images.
  • FIG. 1 it is a schematic flowchart of an intelligent damage assessment method for damaged images according to an embodiment of this application.
  • the method can be executed by a device, and the device can be implemented by software and/or hardware.
  • the method for intelligent damage assessment of damaged images includes:
  • the original damage image set includes, but is not limited to: vehicle damage images, road damage images, and bridge damage images.
  • this application obtains the original damage image set through a picture acquisition device, and the picture acquisition device may be a camera, a mobile phone, or the like.
  • the acquired original damage image collection often has complex morphology characteristics, for example, in the vehicle damage image, the soil and stains on the vehicle are often mistaken for vehicle damage.
  • the large area of soil The stain can also be misidentified as a scratch when it overlaps with the actual scratch in a large area. Therefore, in the embodiment of the present application, the original damage image set is sampled to filter out the rare images in the original damage image set, thereby increasing the probability of obtaining the true value loss image in the original damage image, thereby improving the damage of subsequent images. Loss rate.
  • the rare example image refers to a sample image that is similar to the original damage image but is not the original damage image.
  • the sampling the original damage image set to obtain a sampled image set includes:
  • the historical damage image set means that the images are marked as damage images in advance.
  • the embodiment of the present application uses the following method to calculate the intersection ratio of the original damage image set and the real damage image set of the same damage category as the original damage image set:
  • IOU represents the intersection ratio
  • A represents the area of the original damage image
  • B represents the area of the real damage image.
  • the screening of the difficult images in the original image set according to the intersection ratio includes:
  • intersection ratio is less than the preset intersection ratio threshold, the original damaged image corresponding to the intersection ratio is taken as a rare case image and removed, if the intersection ratio is not less than the preset intersection ratio threshold , The original damaged image corresponding to the intersection ratio is taken as the sampled image.
  • intersection ratio threshold is 0.5.
  • the embodiment of the present application greatly improves the probability of obtaining true damage images in the original damage image set.
  • the feature layer includes a high-level feature layer, a middle-level feature layer, and a bottom-level feature layer.
  • Different feature layers represent image semantic information differently.
  • the high-level feature layer contains more image semantic information, such as image emotion, image Themes, etc.
  • the underlying feature layer contains less image semantic information, such as image shape and image texture.
  • SIFT scale-invariant feature transform
  • the embodiment of the present application presets that the sampled image includes L feature layer images, and the size of the feature layer image with the median number of layers in the L feature layer images is selected as the standard size, and according to the standard size The size of the remaining feature layer images is adjusted, the adjustment of the same size is completed, and the initial damage image set is obtained.
  • the semantic information includes image texture, image theme, and so on.
  • the embodiment of the present application calculates the semantic information of each feature layer in the initial damage image set by the following method:
  • C represents the semantic information weight
  • l min and l max respectively represent the semantic information weight of the lowest feature layer and the semantic information weight of the highest feature layer
  • L represents the number of feature layers
  • c l represents the semantic information of the initial damage image in the initial damage image set.
  • a preset semantic feature enhancement function is used to perform feature enhancement on the semantic information.
  • the preset semantic feature enhancement function includes:
  • y i represents the semantic information weight after feature enhancement
  • x i represents the semantic information that needs feature enhancement
  • x j represents the semantic information weight that does not need feature enhancement
  • w g represents the semantic information bias
  • C(x) Represents the normalization parameter of semantic information
  • e is an infinite non-cyclic decimal.
  • the detection of the damage location of the standard damage image set in the preferred embodiment of the present application includes: calculating the damage value of each characteristic layer in the standard damage image set by using a preset damage function, and selecting the damage A characteristic layer with a value greater than a preset damage threshold is obtained as a damage characteristic layer, and the damage position of the standard damage image set is detected according to the loss characteristic layer.
  • the preset loss function includes:
  • L b (x) represents the damage value
  • x represents the pixel value of the standard damage image set
  • ⁇ and b respectively represent the weight and offset of the standard damage image set
  • C represents the normalization parameter of the standard damage image set.
  • the cropped images in the standard damage image set corresponding to the damage location may be cropped by Cohen-Sutherland cropping, midpoint segmentation cropping algorithm, and Barskey cropping algorithm to obtain the target damage image set.
  • the present application may further include: clustering the standard damage image set using a clustering algorithm according to different types of damage images , In order to improve the detection speed of the damage location in the damage image.
  • the categories of vehicle damage images include: dents, scratches, scratches, and so on.
  • the clustering algorithm may be a k-means clustering algorithm of currently known technologies.
  • S5 Perform damage degree detection on the target damage image set by using the pre-trained image damage degree detection model to obtain the damage degree of the target damage image set.
  • the image damage degree detection model is created based on a convolutional neural network.
  • the image damage degree detection model includes: an input layer, a hidden layer, a fully connected layer, and an output layer.
  • the input layer is used to receive data;
  • the hidden layer includes a convolutional layer, a pooling layer, and an activation layer, which are used to train the data and enhance the expression ability of the model;
  • the fully connected layer is used to hide Data transmission between the layer and the output layer; the output layer is used to output the data after training.
  • the image damage degree detection model completed by pre-training performs damage degree detection on the target damage image set to obtain the damage degree of the target damage image set, including:
  • Receive the target damage image set through the input layer use the hidden layer to train the target damage image set to obtain the training value of the target damage image set, and damage the target through the fully connected layer
  • the training value of the image set is transmitted to the output layer, and the training value of the target damage image set is output by the output layer to obtain the damage degree of the target damage image set.
  • Another embodiment of the present application includes training the image damage degree detection model.
  • the training of the image damage degree detection model includes:
  • the label value refers to the damage degree value of the training image set.
  • the loss function includes:
  • L(s) represents the value of the loss function
  • k represents the number of training image sets
  • y i represents the training value
  • y i ′ represents the label value.
  • the preset threshold is 0.1.
  • the value of the loss function is greater than the preset threshold, readjust the parameters in the image damage degree detection model through a stochastic gradient descent algorithm, and re-adjust the parameters in the image damage degree detection model after the parameter adjustment.
  • the training image set is trained until the loss function value is not greater than the preset threshold, the adjustment of the parameters is ended, the training of the image damage degree detection model is completed, and the trained image damage degree detection model is obtained.
  • the parameters in the image damage degree detection model are weight and bias, and the threshold may be 0.1.
  • the embodiment of the application first samples the original damage image set to obtain the sampled image set, which can filter out the rare images in the original damage image set, and increases the probability of obtaining true damage images in the original damage image set, thereby increasing the original damage image set Second, adjust the size of each feature layer in the sampled image to the same size to obtain the initial damage image set, and calculate the semantic information of each feature layer in the initial damage image set, and combine it with the preset
  • the semantic feature enhancement function of performs feature enhancement on the semantic information to obtain a standard damage image set, which realizes the equalization of the semantic information of all feature layers in the sampled image; further, detecting the damage location of the standard damage image set can be Avoid relying on a pre-built image location damage recognition model for image damage location recognition, thereby improving the detection efficiency of damage locations, cutting out the images in the standard damage image set corresponding to the damage location, and obtaining the target damage image set, which is completed by pre-training
  • the image damage degree detection model of the image damage detection model detects the damage degree of the target
  • FIG. 3 it is a functional block diagram of the intelligent damage assessment device for damaged images of the present application.
  • the damage image intelligent damage assessment device 100 described in this application can be installed in an electronic device.
  • the damage image intelligent loss assessment device may include a sampling module 101, an adjustment module 102, an enhancement module 103, a detection module 104, and a loss assessment module 105.
  • the module described in the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.
  • each module/unit is as follows:
  • the sampling module 101 is configured to obtain an original damage image set, and sample the original damage image set according to the historical damage image set to obtain a sampled image set.
  • the original damage image set includes, but is not limited to: vehicle damage images, road damage images, and bridge damage images.
  • this application obtains the original damage image set through a picture acquisition device, and the picture acquisition device may be a camera, a mobile phone, or the like.
  • the acquired original damage image collection often has complex morphology characteristics, for example, in the vehicle damage image, the soil and stains on the vehicle are often mistaken for vehicle damage.
  • the large area of soil The stain can also be misidentified as a scratch when it overlaps with the actual scratch in a large area. Therefore, in the embodiment of the present application, the original damage image set is sampled to filter out the rare images in the original damage image set, thereby increasing the probability of obtaining the true value loss image in the original damage image, thereby improving the damage of subsequent images. Loss rate.
  • the rare example image refers to a sample image that is similar to the original damage image but is not the original damage image.
  • the sampling the original damage image set to obtain a sampled image set includes:
  • the historical damage image set means that the images are marked as damage images in advance.
  • the embodiment of the present application uses the following method to calculate the intersection ratio of the original damage image set and the real damage image set of the same damage category as the original damage image set:
  • IOU represents the intersection ratio
  • A represents the area of the original damage image
  • B represents the area of the real damage image.
  • the screening of the difficult images in the original image set according to the intersection ratio includes:
  • intersection ratio is less than the preset intersection ratio threshold, the original damaged image corresponding to the intersection ratio is taken as a rare case image and removed, if the intersection ratio is not less than the preset intersection ratio threshold , The original damaged image corresponding to the intersection ratio is taken as the sampled image.
  • intersection ratio threshold is 0.5.
  • the embodiment of the present application greatly improves the probability of obtaining true damage images in the original damage image set.
  • the adjustment module 102 is configured to obtain all the characteristic layers of each sampled image in the sampled image set, and adjust the size of each characteristic layer in the sampled image to the same size to obtain an initial damage image set.
  • the feature layer includes a high-level feature layer, a middle-level feature layer, and a bottom-level feature layer.
  • Different feature layers represent image semantic information differently.
  • the high-level feature layer contains more image semantic information, such as image emotion, image Themes, etc.
  • the underlying feature layer contains less image semantic information, such as image shape and image texture.
  • SIFT scale-invariant feature transform
  • the embodiment of the present application presets that the sampled image includes L feature layer images, and the size of the feature layer image with the median number of layers in the L feature layer images is selected as the standard size, and according to the standard size The size of the remaining feature layer images is adjusted, the adjustment of the same size is completed, and the initial damage image set is obtained.
  • the enhancement module 103 is configured to calculate the semantic information of each feature layer in the initial damage image set, and use a preset semantic feature enhancement function to perform feature enhancement on the semantic information to obtain a standard damage image set.
  • the semantic information includes image texture, image theme, and so on.
  • the embodiment of the present application calculates the semantic information of each feature layer in the initial damage image set by the following method:
  • C represents the semantic information weight
  • l min and l max respectively represent the semantic information weight of the lowest feature layer and the semantic information weight of the highest feature layer
  • L represents the number of feature layers
  • c l represents the semantic information of the initial damage image in the initial damage image set.
  • a preset semantic feature enhancement function is used to perform feature enhancement on the semantic information.
  • the preset semantic feature enhancement function includes:
  • y i represents the semantic information weight after feature enhancement
  • x i represents the semantic information that needs feature enhancement
  • x j represents the semantic information weight that does not need feature enhancement
  • w g represents the semantic information bias
  • C(x) Represents the normalization parameter of semantic information
  • e is an infinite non-cyclic decimal.
  • the detection module 104 is configured to calculate the loss value of each characteristic layer in the standard damage image set, detect the damage position of the standard damage image set according to the loss value, and cut out the standard damage corresponding to the damage position From the images in the image set, the target damage image set is obtained.
  • the detection of the damage position of the standard damage image set in the preferred embodiment of the present application includes:
  • Selection sub-module 1040 used to calculate the damage value of each characteristic layer in the standard damage image set by using a preset damage function, and select the characteristic layer whose damage value is greater than the preset damage threshold to obtain the damage characteristic layer.
  • the damage function is assumed to include:
  • L b (x) represents the damage value
  • x represents the pixel value of the standard damage image set
  • ⁇ and b respectively represent the weight and offset of the standard damage image set
  • C represents the normalization parameter of the standard damage image set
  • the detection sub-module 1041 is used to detect the damage location of the standard damage image set according to the damage feature layer.
  • the cropped images in the standard damage image set corresponding to the damage location may be cropped by Cohen-Sutherland cropping, midpoint segmentation cropping algorithm, and Barskey cropping algorithm to obtain the target damage image set.
  • the present application may further include: clustering the standard damage image set using a clustering algorithm according to different types of damage images , In order to improve the detection speed of the damage location in the damage image.
  • the categories of vehicle damage images include: dents, scratches, scratches, and so on.
  • the clustering algorithm may be a k-means clustering algorithm of currently known technologies.
  • the damage assessment module 105 is used to detect the damage degree of the target damage image set by using a pre-trained image damage degree detection model to obtain the damage degree of the target damage image set.
  • the image damage degree detection model is created based on a convolutional neural network.
  • the image damage degree detection model includes: an input layer, a hidden layer, a fully connected layer, and an output layer.
  • the input layer is used to receive data;
  • the hidden layer includes a convolutional layer, a pooling layer, and an activation layer, which are used to train the data and enhance the expression ability of the model;
  • the fully connected layer is used to hide Data transmission between the layer and the output layer; the output layer is used to output data.
  • the image damage degree detection model completed by pre-training performs damage degree detection on the target damage image set to obtain the damage degree of the target damage image set, including:
  • Receive the target damage image set through the input layer use the hidden layer to train the target damage image set to obtain the training value of the target damage image set, and damage the target through the fully connected layer
  • the training value of the image set is transmitted to the output layer, and the training value of the target damage image set is output by the output layer to obtain the damage degree of the target damage image set.
  • Another embodiment of the present application further includes a model training module for training the image damage degree detection model.
  • the training includes:
  • Step I Obtain the training image set and the label value of the training image set.
  • the label value refers to the damage degree value of the training image set.
  • Step II Input the training image set into the image damage degree detection model for training to obtain training values, and calculate the loss function values of the training values and the label values through a loss function.
  • the loss function includes:
  • L(s) represents the value of the loss function
  • k represents the number of training image sets
  • y i represents the training value
  • y i ′ represents the label value.
  • the preset threshold is 0.1.
  • Step III When the value of the loss function is greater than the preset threshold, the parameters in the image damage degree detection model are re-adjusted through the stochastic gradient descent algorithm, and the image damage degree detection model after the parameter adjustment is used to re-adjust all the parameters.
  • the training image set is trained until the loss function value is not greater than the preset threshold, the adjustment of the parameters is ended, the training of the image damage degree detection model is completed, and the trained image damage degree detection model is obtained .
  • the parameters in the image damage degree detection model are weight and bias, and the threshold may be 0.1.
  • the embodiment of the application first samples the original damage image set to obtain the sample image set, which can filter out the rare images in the original damage image set, and improves the probability of obtaining the true damage image in the original damage image set; secondly, the sampled image
  • the size of each feature layer in the set is adjusted to the same size to obtain the initial damage image set, and the semantic information of each feature layer in the initial damage image set is calculated, and the semantic information is characterized in combination with the preset semantic feature enhancement function Reinforce, obtain the standard damage image set, realize the equalization of the semantic information of all the feature layers in the sampled image, so as to make full use of all the semantic information in the sampled image, and then ensure the accuracy of the loss assessment of the sampled image;
  • the damage location of the standard damage image set is detected, and the images in the standard damage image set corresponding to the damage location are cut out to obtain the target damage image set, and the target damage image set is determined by the pre-trained image damage detection model.
  • the damage degree detection ensures that there will be no deviation of the
  • FIG. 4 it is a schematic diagram of the structure of an electronic device that implements the method for intelligent damage assessment of damaged images according to the present application.
  • the electronic device 1 may include a processor 10, a memory 11, and a bus, and may also include a computer program stored in the memory 11 and running on the processor 10, such as a damage image intelligent loss assessment program.
  • the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, magnetic disk, CD etc.
  • the memory 11 may be an internal storage unit of the electronic device 1 in some embodiments, for example, a mobile hard disk of the electronic device 1.
  • the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart media card (SMC), and a secure digital (Secure Digital) equipped on the electronic device 1. , SD) card, flash card (Flash Card), etc.
  • the memory 11 may also include both an internal storage unit of the electronic device 1 and an external storage device.
  • the memory 11 can be used not only to store application software and various data installed in the electronic device 1, such as the code of an intelligent damage assessment program for damage images, etc., but also to temporarily store data that has been output or will be output.
  • the processor 10 may be composed of integrated circuits in some embodiments, for example, may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits with the same function or different functions, including one or more Combinations of central processing unit (CPU), microprocessor, digital processing chip, graphics processor, and various control chips, etc.
  • the processor 10 is the control unit of the electronic device, which uses various interfaces and lines to connect the various components of the entire electronic device, and runs or executes programs or modules (such as damage Image intelligent loss assessment program, etc.), and call the data stored in the memory 11 to execute various functions of the electronic device 1 and process data.
  • programs or modules such as damage Image intelligent loss assessment program, etc.
  • the bus may be a peripheral component interconnect standard (PCI) bus or an extended industry standard architecture (EISA) bus, etc.
  • PCI peripheral component interconnect standard
  • EISA extended industry standard architecture
  • the bus can be divided into address bus, data bus, control bus and so on.
  • the bus is configured to implement connection and communication between the memory 11 and at least one processor 10 and the like.
  • FIG. 4 only shows an electronic device with components. Those skilled in the art can understand that the structure shown in FIG. 4 does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure. Components, or a combination of certain components, or different component arrangements.
  • the electronic device 1 may also include a power source (such as a battery) for supplying power to various components.
  • the power source may be logically connected to the at least one processor 10 through a power management device, thereby controlling power
  • the device implements functions such as charge management, discharge management, and power consumption management.
  • the power supply may also include any components such as one or more DC or AC power supplies, recharging devices, power failure detection circuits, power converters or inverters, and power status indicators.
  • the electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.
  • the electronic device 1 may also include a network interface.
  • the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is usually used in the electronic device 1 Establish a communication connection with other electronic devices.
  • the electronic device 1 may also include a user interface.
  • the user interface may be a display (Display) and an input unit (such as a keyboard (Keyboard)).
  • the user interface may also be a standard wired interface or a wireless interface.
  • the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc.
  • the display can also be appropriately called a display screen or a display unit, which is used to display the information processed in the electronic device 1 and to display a visualized user interface.
  • the damage image intelligent loss assessment program 12 stored in the memory 11 in the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can realize:
  • the damage degree detection is performed on the target damage image set through the pre-trained image damage degree detection model to obtain the damage degree of the target damage image set.
  • the integrated module/unit of the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.
  • the computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory) ).
  • the computer-readable storage medium may be non-volatile or volatile.
  • the computer-readable storage medium may mainly include a storage program area and a storage data area, where the storage program area may store an operating system, an application program required by at least one function, etc.; the storage data area may store Data created by the use of nodes, etc.
  • modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the objectives of the solutions of the embodiments.
  • the functional modules in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the above-mentioned integrated unit may be implemented in the form of hardware, or may be implemented in the form of hardware plus software functional modules.
  • the blockchain referred to in this application is a new application mode of computer technology such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm.
  • Blockchain essentially a decentralized database, is a series of data blocks associated with cryptographic methods. Each data block contains a batch of network transaction information for verification. The validity of the information (anti-counterfeiting) and the generation of the next block.
  • the blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.

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Abstract

一种损伤图像智能定损方法，包括：获取原始损伤图像集，根据历史损伤图像集对所述原始损伤图像集进行采样，得到采样图像集（S1）；获取所述采样图像集中每个采样图像的所有特征层，将所述采样图像中每个特征层的尺寸调整为相同尺寸，得到初始损伤图像集（S2）；计算所述初始损伤图像集中每个特征层的语义信息，利用预设的语义特征强化函数对所述语义信息进行特征强化，得到标准损伤图像集（S3）；计算所述标准损伤图像集中每个特征层的损伤值，根据所述损伤值检测所述标准损伤图像集的损伤位置，并裁剪出所述损伤位置对应的标准损伤图像集中的图像，得到目标损伤图像集（S4）；通过预先训练完成的图像损伤程度检测模型对所述目标损伤图像集进行损伤程度检测，得到所述目标损伤图像集的损伤程度（S5）。该方法还涉及区块链技术，用户的隐私信息可存储于区块链节点中。该方法可以提高损伤图像的定损效率和检测效率。

Description

损伤图像智能定损方法、装置、电子设备及存储介质
本申请要求于2020年04月30日提交中国专利局、申请号为202010361298.4、发明名称为“损伤图像智能定损方法、装置、电子设备及存储介质”的中国专利申请的优先权，其全部内容通过引用结合在申请中。
技术领域
本申请涉及人工智能技术领域，尤其涉及一种损伤图像智能定损方法、装置、电子设备及计算机可读存储介质。
背景技术
损伤图像是指对存在损伤的目标物拍摄得到的图像，例如，车辆事故中所拍摄的事故车辆图像、道路发生坍塌时的所拍摄的道路图像等。损伤图像一般用于事后定损，如保险公司根据所述损伤图像对出现事故的车辆进行定损，以进行保险赔付等。
发明人意识到，由于损伤图像中通常会存在与真实损伤图像相似却不是真实损伤图像的难例，例如，对于车辆损伤图像来说，车辆部件中含有的泥土、污渍经常会被当做图像损伤，目前，主要依赖随机法获取损伤图像中的真实损伤图像，但是随机法具有不稳定的特性，容易影响损伤图像中真实损伤图像的获取概率，从而会影响损伤图像的定损效率。
另外，对损伤图像的定损通常需要对定损图像进行识别来确定目标物的受损位置，而受损位置识别的准确度主要依赖对定损图像进行识别的模型。目前业内所使用的模型主要是预先收集各种图像的外观数据进行学习，然后利用构建的图像位置损伤识别模型识别定损图像中的损伤位置。为了保障识别精度，通常需要尽可能多的获取各种目标物的外观图像数据作为样本图像进行训练，而且模型算法的训练和参数优化过程周期通常较长，整体实现成本较大。因此，在损伤图像定损的处理中，还需要一种高效识别图像损伤位置的定损方案。
发明内容
本申请提供一种损伤图像智能定损的方法、装置、电子设备及计算机可读存储介质，其主要目的在于提高损伤图像的定损效率以及损伤位置的检测效率。
为实现上述目的，本申请提供的一种损伤图像智能定损方法，包括：
获取原始损伤图像集，根据历史损伤图像集对所述原始损伤图像集进行采样，得到采样图像集；
获取所述采样图像集中每个采样图像的所有特征层，将所述采样图像中每个特征层的尺寸调整为相同尺寸，得到初始损伤图像集；
计算所述初始损伤图像集中每个特征层的语义信息，利用预设的语义特征强化函数对所述语义信息进行特征强化，得到标准损伤图像集；
所述计算所述标准损伤图像集中每个特征层的损伤值，根据所述损伤值检测所述标准损伤图像集的损伤位置，并裁剪出所述损伤位置对应的标准损伤图像集中的图像，得到目标损伤图像集；
通过预先训练完成的图像损伤程度检测模型对所述目标损伤图像集进行损伤程度检测，得到所述目标损伤图像集的损伤程度。
为了解决上述问题，本申请还提供一种电子设备，所述电子设备包括：
至少一个处理器；以及，
与所述至少一个处理器通信连接的存储器；其中，
所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行如下所述的损伤图像智能定损方法：
获取原始损伤图像集，根据历史损伤图像集对所述原始损伤图像集进行采样，得到采样图像集；
获取所述采样图像集中每个采样图像的所有特征层，将所述采样图像中每个特征层的尺寸调整为相同尺寸，得到初始损伤图像集；
计算所述初始损伤图像集中每个特征层的语义信息，利用预设的语义特征强化函数对所述语义信息进行特征强化，得到标准损伤图像集；
计算所述标准损伤图像集中每个特征层的损伤值，根据所述损伤值检测所述标准损伤图像集的损伤位置，并裁剪出所述损伤位置对应的标准损伤图像集中的图像，得到目标损伤图像集；
通过预先训练完成的图像损伤程度检测模型对所述目标损伤图像集进行损伤程度检测，得到所述目标损伤图像集的损伤程度。
为了解决上述问题，本申请还提供一种计算机可读存储介质，包括存储数据区和存储程序区，存储数据区存储根据区块链节点的使用所创建的数据，存储程序区存储有计算机程序，其中，所述计算机程序被处理器执行时实现如下所述的损伤图像智能定损方法：
获取原始损伤图像集，根据历史损伤图像集对所述原始损伤图像集进行采样，得到采样图像集；
获取所述采样图像集中每个采样图像的所有特征层，将所述采样图像中每个特征层的尺寸调整为相同尺寸，得到初始损伤图像集；
计算所述初始损伤图像集中每个特征层的语义信息，利用预设的语义特征强化函数对所述语义信息进行特征强化，得到标准损伤图像集；
计算所述标准损伤图像集中每个特征层的损伤值，根据所述损伤值检测所述标准损伤图像集的损伤位置，并裁剪出所述损伤位置对应的标准损伤图像集中的图像，得到目标损伤图像集；
通过预先训练完成的图像损伤程度检测模型对所述目标损伤图像集进行损伤程度检测，得到所述目标损伤图像集的损伤程度。
为了解决上述问题，本申请还提供一种损伤图像智能定损装置，所述装置包括：
采样模块，用于获取原始损伤图像集，根据历史损伤图像集对所述原始损伤图像集进行采样，得到采样图像集；
调整模块，用于获取所述采样图像集中每个采样图像的所有特征层，将所述采样图像中每个特征层的尺寸调整为相同尺寸，得到初始损伤图像集；
强化模块，用于计算所述初始损伤图像集中每个特征层的语义信息，用于利用预设的语义特征强化函数对所述语义信息进行特征强化，得到标准损伤图像集；
检测模块，用于计算所述标准损伤图像集中每个特征层的损伤值，根据所述损伤值检测所述标准损伤图像集的损伤位置，并裁剪出所述损伤位置对应的标准损伤图像集中的图像，得到目标损伤图像集；
定损模块，用于通过预先训练完成的图像损伤程度检测模型对所述目标损伤图像集进行损伤程度检测，得到所述目标损伤图像集的损伤程度。
本申请实施例首先对原始损伤图像集进行采样，得到采样图像集，可以筛选出原始损伤图像集中的难例图像，提高了原始损伤图像集中真实损伤图像的获取概率，从而提高了原始损伤图像集的定损效率；其次，本申请实施例将所述采样图像中每个特征层的尺寸调整为相同尺寸，得到初始损伤图像集，并计算所述初始损伤图像集中每个特征层的语义信息，以及结合预设的语义特征强化函数对所述语义信息进行特征强化，得到标准损伤图像 集，实现了采样图像中所有特征层的语义信息的均衡化；进一步地，本申请实施例通过每个特征层的损伤值检测所述标准损伤图像集的损伤位置，不需要构建及训练图像位置损伤识别模型，从而可以提高损伤位置的检测效率；此外，本申请实施例裁剪出所述损伤位置对应的标准损伤图像集中的图像，得到目标损伤图像集，通过预先训练完成的图像损伤程度检测模型对所述目标损伤图像集进行损伤程度检测，即本申请实施例只利用图像损伤程度检测模型对损伤位置进行损伤程度的检测，保证了不会因人工主观原因出现图像定损偏差的现象，并进一步提高了计算效率。因此，本申请实施例提出的一种损伤图像智能定损方法、装置、电子设备以及计算机可读存储介质可以提高损伤图像的定损效率以及损伤位置的检测效率。
附图说明
图1为本申请一实施例提供的损伤图像智能定损方法的流程示意图；
图2为本申请图1中损伤图像智能定损方法中步骤S5的详细实施流程示意图；
图3为本申请一实施例提供的损伤图像智能定损装置的模块示意图；
图4为本申请一实施例提供的实现损伤图像智能定损方法的电子设备的内部结构示意图；
本申请目的的实现、功能特点及优点将整合实施例，参照附图做进一步说明。
具体实施方式
应当理解，此处所描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。
本申请实施例提供的损伤图像智能定损方法的执行主体包括但不限于服务端、终端等能够被配置为执行本申请实施例提供的该方法的电子设备中的至少一种。换言之，所述损伤图像智能定损方法可以由安装在终端设备或服务端设备的软件或硬件来执行，所述软件可以是区块链平台。所述服务端包括但不限于：单台服务器、服务器集群、云端服务器或云端服务器集群等。
区块链是分布式数据存储、点对点传输、共识机制、加密算法等计算机技术的新型应用模式。区块链(Blockchain)，本质上是一个去中心化的数据库，是一串使用密码学方法相关联产生的数据块，每一个数据块中包含了一批次网络交易的信息，用于验证其信息的有效性(防伪)和生成下一个区块。区块链可以包括区块链底层平台、平台产品服务层以及应用服务层。
区块链底层平台可以包括用户管理、基础服务、智能合约以及运营监控等处理模块。其中，用户管理模块负责所有区块链参与者的身份信息管理，包括维护公私钥生成(账户管理)、密钥管理以及用户真实身份和区块链地址对应关系维护(权限管理)等，并且在授权的情况下，监管和审计某些真实身份的交易情况，提供风险控制的规则配置(风控审计)；基础服务模块部署在所有区块链节点设备上，用来验证业务请求的有效性，并对有效请求完成共识后记录到存储上，对于一个新的业务请求，基础服务先对接口适配解析和鉴权处理(接口适配)，然后通过共识算法将业务信息加密(共识管理)，在加密之后完整一致的传输至共享账本上(网络通信)，并进行记录存储；智能合约模块负责合约的注册发行以及合约触发和合约执行，开发人员可以通过某种编程语言定义合约逻辑，发布到区块链上(合约注册)，根据合约条款的逻辑，调用密钥或者其它的事件触发执行，完成合约逻辑，同时还提供对合约升级注销的功能；运营监控模块主要负责产品发布过程中的部署、配置的修改、合约设置、云适配以及产品运行中的实时状态的可视化输出，例如：告警、监控网络情况、监控节点设备健康状态等。
本申请提供一种损伤图像智能定损的方法。参照图1所示，为本申请一实施例提供的损伤图像智能定损方法的流程示意图。该方法可以由一个装置执行，该装置可以由软件和/或硬件实现。
在本实施例中，损伤图像智能定损的方法包括：
S1、获取原始损伤图像集，根据历史损伤图像集对所述原始损伤图像集进行采样，得到采样图像集。
在本申请的至少一个实施例中，所述原始损伤图像集包括，但不限于：车辆损伤图像、道路损伤图像以及桥梁损伤图像等。较佳地，本申请通过图片采集设备获取所述原始损伤图像集，所述图片采集设备可以为摄像机、手机等。
进一步地，由于获取的原始损伤图像集中往往具有形态复杂的特点，比如，在车辆损伤图像中，车辆上的泥土、污渍等往往会被误认为车辆损伤，同时，车辆上含有的大面积泥土、污渍在和真实擦伤地方发生大面积重叠时也会误识别成擦伤。因此，本申请实施例通过对所述原始损伤图像集进行采样，以筛选出原始损失图像集中存在的难例图像，从而提高获取原始损伤图像中真值损失图像的概率，进而提高后续图像损伤的定损率。所述难例图像指的是与原始损伤图像相似却不是原始损伤图像的样例图像。
具体的，所述对所述原始损伤图像集进行采样，得到采样图像集，包括：
计算所述原始损伤图像集与所述原始损伤图像集损伤类别相同的历史损伤图像集的交并比值，根据所述交并比值筛选出所述原始图像集中的难例图像并剔除，得到所述采样图像集。其中，所述历史损伤图像集表示预先对图像标记为损伤图像。
详细地，本申请实施例通过下述方法计算所述原始损伤图像集与所述原始损伤图像集损伤类别相同的真实损伤图像集的交并比值：
Figure PCTCN2020098972-appb-000001
其中，IOU表示交并比值，A表示原始损伤图像的面积，B表示真实损伤图像的面积。
进一步地，所述根据所述交并比值筛选出所述原始图像集中的难例图像包括：
若所述交并比值小于预设的交并比阈值时，则将所述交并比值对应的原始损伤图像作为难例图像并剔除，若所述交并比值不小于预设的交并比阈值，则将所述交并比值对应的原始损伤图像作为采样图像。
可选的，所述交并比阈值为0.5。
通过对原始损伤图像集进行采样，筛选出原始损伤图像集中的难例图像，本申请实施例大大提高了原始损伤图像集中真实损伤图像的获取概率。
S2、获取所述采样图像集中每个采样图像的所有特征层，并将所述采样图像中每个特征层的尺寸调整为相同尺寸，得到初始损伤图像集。
所述特征层包括高层特征层，中层特征层以及底层特征层等，不同的特征层表征图像语义信息也有所不同，例如，所述高层特征层包含更多的图像语义信息，比如图像情感，图像主题等，所述底层特征层包含较少的图像语义信息，比如图像形状，图像纹理等。本申请实施例采用尺度不变特征转换(Scale-invariant feature transform，SIFT)算法获取所述采样图像集中每个采样图像的所有特征层。
进一步地，本申请实施例预设所述采样图像包含L个特征层图像，选取所述L个特征层图像中层数为中位数的特征层图像的尺寸作为标准尺寸，根据所述标准尺寸调整剩余的特征层图像的尺寸，完成所述相同尺寸的调整，得到所述初始损伤图像集。
S3、计算所述初始损伤图像集中每个特征层的语义信息，利用预设的语义特征强化函数对所述语义信息进行特征强化，得到标准损伤图像集。
所述语义信息包括图像纹理、图像主题等。
较佳地，本申请实施例通过下述方法计算所述初始损伤图像集中每个特征层的语义信息：
Figure PCTCN2020098972-appb-000002
其中，C表示语义信息权重，l min,l max分别表示最低特征层的语义信息和最高特征层的语义信息权重，L表示特征层的数量，c l表示初始损伤图像集中初始损伤图像的语义信息权重。
在本申请的至少一个实施例中，为了更好的识别出所述语义信息所要表达的图像特征信息，利用预设的语义特征强化函数对所述语义信息进行特征强化。
其中，所述预设的语义特征强化函数包括：
Figure PCTCN2020098972-appb-000003
其中，y i表示特征强化后的语义信息权重，x i表示需要进行特征强化的语义信息，x j表示不需要进行特征强化的语义信息权重，w g表示语义信息的偏置，C(x)表示语义信息的归一化参数，e为无限不循环小数。
S4、计算所述标准损伤图像集中每个特征层的损伤值，根据所述损伤值检测所述标准损伤图像集的损伤位置，并裁剪出所述损伤位置对应的标准损伤图像集中的图像，得到目标损伤图像集。
较佳地，本申请较佳实施例所述检测所述标准损伤图像集的损伤位置，包括：利用预设的损伤函数计算所述标准损伤图像集中每个特征层的损伤值，选取所述损伤值大于预设损伤阈值的特征层，得到损伤特征层，根据所述损失特征层，检测出所述标准损伤图像集的损伤位置。
其中，所述预设的损失函数包括：
Figure PCTCN2020098972-appb-000004
γ＝αln(b+1)
其中，L b(x)表示损伤值，x表示标准损伤图像集的像素值，α和b分别表示标准损伤图像集的权重和偏置，C表示标准损伤图像集的归一化参数。
进一步地，所述裁剪出所述损伤位置对应的标准损伤图像集中的图像可以通过Cohen-Sutherland裁剪、中点分割裁剪算法以及Barskey裁剪等算法进行裁剪，得到所述目标损伤图像集。
进一步地，本申请其他实施例中，在检测所述标准损伤图像集的损伤位置之前，本申请还可以包括：根据损伤图像的类别不同，利用聚类算法对所述标准损伤图像集进行聚类，以提高损伤图像中损伤位置的检测速度。例如，车辆损伤图像的类别包括：凹陷、刮伤以及刮痕等等。
可选地，所述聚类算法可以为当前已知技术的k-means聚类算法。
S5、通过预先训练完成的图像损伤程度检测模型对所述目标损伤图像集进行损伤程度检测，得到所述目标损伤图像集的损伤程度。
所述图像损伤程度检测模型基于卷积神经网络进行创建的。本申请较佳实施例中，所述图像损伤程度检测模型包括：输入层、隐藏层、全连接层以及输出层。其中，所述输入层用于接收数据；所述隐藏层包括卷积层、池化层以及激活层，用于对所述数据进行训练以及增强模型的表达能力；所述全连接层用于隐藏层和输出层之间的数据传输；所述输出层用于输出训练后的数据。
进一步地，所述通过预先训练完成的图像损伤程度检测模型对所述目标损伤图像集进行损伤程度检测，得到所述目标损伤图像集的损伤程度，包括：
通过所述输入层接收所述目标损伤图像集，利用所述隐藏层对所述目标损伤图像集进行训练，得到所述目标损伤图像集的训练值，通过所述全连接层将所述目标损伤图像集的 训练值传输至所述输出层，根据所述输出层输出所述目标损伤图像集的训练值，得到所述目标损伤图像集的损伤程度。
在本申请的另一个实施例包括训练所述图像损伤程度检测模型。参阅图2所示，所述训练所述图像损伤程度检测模型，包括：
S50、获取训练图像集以及所述训练图像集的标签值。
本申请实施例中，所述标签值指的是所述训练图像集的损伤程度值。
S51、将所述训练图像集输入至所述图像损伤程度检测模型中进行训练，得到训练值，并通过一个损失函数计算所述训练值与所述标签值的损失函数值。
本申请较佳实时例中，所述损失函数包括：
Figure PCTCN2020098972-appb-000005
其中，L(s)表示损失函数值，k表示训练图像集的数量，y i表示训练值，y i′表示标签值。所述预设的阈值为0.1。
S52、若所述损失函数值大于预设的阈值时，通过随机梯度下降算法重新调整所述图像损伤程度检测模型中的参数，并利用参数调整后的所述图像损伤程度检测模型重新对所述训练图像集进行训练，直至所述损失函数值不大于所述预设的阈值时，结束所述参数的调整，完成所述图像损伤程度检测模型的训练，得到训练完成的图像损伤程度检测模型。
其中，所述图像损伤程度检测模型中的参数为权重和偏置，所述阈值可以为0.1。
本申请实施例首先对原始损伤图像集进行采样，得到采样图像集，可以筛选出原始损伤图像集中的难例图像，提高了原始损伤图像集中真实损伤图像的获取概率，从而提高了原始损伤图像集的定损效率；其次，将所述采样图像中每个特征层的尺寸调整为相同尺寸，得到初始损伤图像集，并计算所述初始损伤图像集中每个特征层的语义信息，以及结合预设的语义特征强化函数对所述语义信息进行特征强化，得到标准损伤图像集，实现了采样图像中所有特征层的语义信息的均衡化；进一步地，检测所述标准损伤图像集的损伤位置，可以避免依赖预先构建的图像位置损伤识别模型进行图像损伤位置识别，从而可以提高损伤位置的检测效率，裁剪出所述损伤位置对应的标准损伤图像集中的图像，得到目标损伤图像集，通过预先训练完成的图像损伤程度检测模型对所述目标损伤图像集进行损伤程度检测，保证了不会因人工主观原因出现图像定损偏差的现象。因此，本申请实施例提出的损伤图像智能定损方法可以提高损伤图像的定损效率以及损伤位置的检测效率。
如图3所示，是本申请损伤图像智能定损装置的功能模块图。
本申请所述损伤图像智能定损装置100可以安装于电子设备中。根据实现的功能，所述损伤图像智能定损装置可以包括采样模块101、调整模块102、强化模块103、检测模块104以及定损模块105。本发所述模块也可以称之为单元，是指一种能够被电子设备处理器所执行，并且能够完成固定功能的一系列计算机程序段，其存储在电子设备的存储器中。
在本实施例中，关于各模块/单元的功能如下：
所述采样模块101，用于获取原始损伤图像集，根据历史损伤图像集对所述原始损伤图像集进行采样，得到采样图像集。
在本申请的至少一个实施例中，所述原始损伤图像集包括，但不限于：车辆损伤图像、道路损伤图像以及桥梁损伤图像等。较佳地，本申请通过图片采集设备获取所述原始损伤图像集，所述图片采集设备可以为摄像机、手机等。
进一步地，由于获取的原始损伤图像集中往往具有形态复杂的特点，比如，在车辆损伤图像中，车辆上的泥土、污渍等往往会被误认为车辆损伤，同时，车辆上含有的大面积泥土、污渍在和真实擦伤地方发生大面积重叠时也会误识别成擦伤。因此，本申请实施例通过对所述原始损伤图像集进行采样，以筛选出原始损失图像集中存在的难例图像，从而 提高获取原始损伤图像中真值损失图像的概率，进而提高后续图像损伤的定损率。所述难例图像指的是与原始损伤图像相似却不是原始损伤图像的样例图像。
具体的，所述对所述原始损伤图像集进行采样，得到采样图像集，包括：
计算所述原始损伤图像集与所述原始损伤图像集损伤类别相同的历史损伤图像集的交并比值，根据所述交并比值筛选出所述原始图像集中的难例图像并剔除，得到所述采样图像集。其中，所述历史损伤图像集表示预先对图像标记为损伤图像。
详细地，本申请实施例通过下述方法计算所述原始损伤图像集与所述原始损伤图像集损伤类别相同的真实损伤图像集的交并比值：
Figure PCTCN2020098972-appb-000006
其中，IOU表示交并比值，A表示原始损伤图像的面积，B表示真实损伤图像的面积。
进一步地，所述根据所述交并比值筛选出所述原始图像集中的难例图像包括：
若所述交并比值小于预设的交并比阈值时，则将所述交并比值对应的原始损伤图像作为难例图像并剔除，若所述交并比值不小于预设的交并比阈值，则将所述交并比值对应的原始损伤图像作为采样图像。
可选的，所述交并比阈值为0.5。
通过对原始损伤图像集进行采样，筛选出原始损伤图像集中的难例图像，本申请实施例大大提高了原始损伤图像集中真实损伤图像的获取概率。
所述调整模块102，用于获取所述采样图像集中每个采样图像的所有特征层，并将所述采样图像中每个特征层的尺寸调整为相同尺寸，得到初始损伤图像集。
所述特征层包括高层特征层，中层特征层以及底层特征层等，不同的特征层表征图像语义信息也有所不同，例如，所述高层特征层包含更多的图像语义信息，比如图像情感，图像主题等，所述底层特征层包含较少的图像语义信息，比如图像形状，图像纹理等。本申请实施例采用尺度不变特征转换(Scale-invariant feature transform，SIFT)算法获取所述采样图像集中每个采样图像的所有特征层。
进一步地，本申请实施例预设所述采样图像包含L个特征层图像，选取所述L个特征层图像中层数为中位数的特征层图像的尺寸作为标准尺寸，根据所述标准尺寸调整剩余的特征层图像的尺寸，完成所述相同尺寸的调整，得到所述初始损伤图像集。
所述强化模块103，用于计算所述初始损伤图像集中每个特征层的语义信息，利用预设的语义特征强化函数对所述语义信息进行特征强化，得到标准损伤图像集。
所述语义信息包括图像纹理、图像主题等。
较佳地，本申请实施例通过下述方法计算所述初始损伤图像集中每个特征层的语义信息：
Figure PCTCN2020098972-appb-000007
其中，C表示语义信息权重，l min,l max分别表示最低特征层的语义信息和最高特征层的语义信息权重，L表示特征层的数量，c l表示初始损伤图像集中初始损伤图像的语义信息权重。
在本申请的至少一个实施例中，为了更好的识别出所述语义信息所要表达的图像特征信息，利用预设的语义特征强化函数对所述语义信息进行特征强化。
其中，所述预设的语义特征强化函数包括：
Figure PCTCN2020098972-appb-000008
其中，y i表示特征强化后的语义信息权重，x i表示需要进行特征强化的语义信息，x j表 示不需要进行特征强化的语义信息权重，w g表示语义信息的偏置，C(x)表示语义信息的归一化参数，e为无限不循环小数。
所述检测模块104，用于计算所述标准损伤图像集中每个特征层的损失值，根据所述损失值检测所述标准损伤图像集的损伤位置，并裁剪出所述损伤位置对应的标准损伤图像集中的图像，得到目标损伤图像集。
较佳地，本申请较佳实施例所述检测所述标准损伤图像集的损伤位置，包括：
选取子模块1040：用于利用预设的损伤函数计算所述标准损伤图像集中每个特征层的损伤值，选取所述损伤值大于预设损伤阈值的特征层，得到损伤特征层，所述预设的损伤函数包括：
Figure PCTCN2020098972-appb-000009
γ＝αln(b+1)
其中，L b(x)表示损伤值，x表示标准损伤图像集的像素值，α和b分别表示标准损伤图像集的权重和偏置，C表示标准损伤图像集的归一化参数；
检测子模块1041：用于根据所述损伤特征层，检测出所述标准损伤图像集的损伤位置。
进一步地，所述裁剪出所述损伤位置对应的标准损伤图像集中的图像可以通过Cohen-Sutherland裁剪、中点分割裁剪算法以及Barskey裁剪等算法进行裁剪，得到所述目标损伤图像集。
进一步地，本申请其他实施例中，在检测所述标准损伤图像集的损伤位置之前，本申请还可以包括：根据损伤图像的类别不同，利用聚类算法对所述标准损伤图像集进行聚类，以提高损伤图像中损伤位置的检测速度。例如，车辆损伤图像的类别包括：凹陷、刮伤以及刮痕等等。
可选地，所述聚类算法可以为当前已知技术的k-means聚类算法。
所述定损模块105，用于通过预先训练完成的图像损伤程度检测模型对所述目标损伤图像集进行损伤程度检测，得到所述目标损伤图像集的损伤程度。
所述图像损伤程度检测模型基于卷积神经网络进行创建的。本申请较佳实施例中，所述图像损伤程度检测模型包括：输入层、隐藏层、全连接层以及输出层。其中，所述输入层用于接收数据；所述隐藏层包括卷积层、池化层以及激活层，用于对所述数据进行训练以及增强模型的表达能力；所述全连接层用于隐藏层和输出层之间的数据传输；所述输出层用于输出数据。
进一步地，所述通过预先训练完成的图像损伤程度检测模型对所述目标损伤图像集进行损伤程度检测，得到所述目标损伤图像集的损伤程度，包括：
通过所述输入层接收所述目标损伤图像集，利用所述隐藏层对所述目标损伤图像集进行训练，得到所述目标损伤图像集的训练值，通过所述全连接层将所述目标损伤图像集的训练值传输至所述输出层，根据所述输出层输出所述目标损伤图像集的训练值，得到所述目标损伤图像集的损伤程度。
在本申请的另一个实施例还包括模型训练模块，用于训练所述图像损伤程度检测模型详细地，所述训练包括：
步骤Ⅰ、获取训练图像集以及所述训练图像集的标签值。
本申请实施例中，所述标签值指的是所述训练图像集的损伤程度值。
步骤Ⅱ、将所述训练图像集输入至所述图像损伤程度检测模型中进行训练，得到训练值，并通过一个损失函数计算所述训练值与所述标签值的损失函数值。
本申请较佳实时例中，所述损失函数包括：
Figure PCTCN2020098972-appb-000010
其中，L(s)表示损失函数值，k表示训练图像集的数量，y i表示训练值，y i′表示标签值。所述预设的阈值为0.1。
步骤Ⅲ、在所述损失函数值大于预设的阈值时，通过随机梯度下降算法重新调整所述图像损伤程度检测模型中的参数，并利用参数调整后的所述图像损伤程度检测模型重新对所述训练图像集进行训练，直至所述损失函数值不大于所述预设的阈值时，结束所述参数的调整，完成所述图像损伤程度检测模型的训练，得到训练完成的图像损伤程度检测模型。
其中，所述图像损伤程度检测模型中的参数为权重和偏置，所述阈值可以为0.1。
本申请实施例首先对原始损伤图像集进行采样，得到采样图像集，可以筛选出原始损伤图像集中的难例图像，提高了原始损伤图像集中真实损伤图像的获取概率；其次，将所述采样图像中每个特征层的尺寸调整为相同尺寸，得到初始损伤图像集，并计算所述初始损伤图像集中每个特征层的语义信息，以及结合预设的语义特征强化函数对所述语义信息进行特征强化，得到标准损伤图像集，实现了采样图像中所有特征层的语义信息的均衡化，从而充分的利用了采样图像中所有的语义信息，进而保证了采样图像的定损准确率；进一步地，检测所述标准损伤图像集的损伤位置，并裁剪出所述损伤位置对应的标准损伤图像集中的图像，得到目标损伤图像集，通过预先训练完成的图像损伤程度检测模型对所述目标损伤图像集进行损伤程度检测，保证了不会因人工主观原因出现图像定损偏差的现象。因此，本申请实施例提出的一种损伤图像智能定损装置可以提高损伤图像定损的准确率。
如图4所示，是本申请实现损伤图像智能定损的方法的电子设备的结构示意图。
所述电子设备1可以包括处理器10、存储器11和总线，还可以包括存储在所述存储器11中并可在所述处理器10上运行的计算机程序，如损伤图像智能定损程序。
其中，所述存储器11至少包括一种类型的可读存储介质，所述可读存储介质包括闪存、移动硬盘、多媒体卡、卡型存储器(例如：SD或DX存储器等)、磁性存储器、磁盘、光盘等。所述存储器11在一些实施例中可以是电子设备1的内部存储单元，例如该电子设备1的移动硬盘。所述存储器11在另一些实施例中也可以是电子设备1的外部存储设备，例如电子设备1上配备的插接式移动硬盘、智能存储卡(Smart Media Card，SMC)、安全数字(Secure Digital，SD)卡、闪存卡(Flash Card)等。进一步地，所述存储器11还可以既包括电子设备1的内部存储单元也包括外部存储设备。所述存储器11不仅可以用于存储安装于电子设备1的应用软件及各类数据，例如损伤图像智能定损程序的代码等，还可以用于暂时地存储已经输出或者将要输出的数据。
所述处理器10在一些实施例中可以由集成电路组成，例如可以由单个封装的集成电路所组成，也可以是由多个相同功能或不同功能封装的集成电路所组成，包括一个或者多个中央处理器(Central Processing unit，CPU)、微处理器、数字处理芯片、图形处理器及各种控制芯片的组合等。所述处理器10是所述电子设备的控制核心(Control Unit)，利用各种接口和线路连接整个电子设备的各个部件，通过运行或执行存储在所述存储器11内的程序或者模块(例如损伤图像智能定损程序等)，以及调用存储在所述存储器11内的数据，以执行电子设备1的各种功能和处理数据。
所述总线可以是外设部件互连标准(peripheral component interconnect，简称PCI)总线或扩展工业标准结构(extended industry standard architecture，简称EISA)总线等。该总线可以分为地址总线、数据总线、控制总线等。所述总线被设置为实现所述存储器11以及至少一个处理器10等之间的连接通信。
图4仅示出了具有部件的电子设备，本领域技术人员可以理解的是，图4示出的结构并不构成对所述电子设备1的限定，可以包括比图示更少或者更多的部件，或者组合某些 部件，或者不同的部件布置。
例如，尽管未示出，所述电子设备1还可以包括给各个部件供电的电源(比如电池)，优选地，电源可以通过电源管理装置与所述至少一个处理器10逻辑相连，从而通过电源管理装置实现充电管理、放电管理、以及功耗管理等功能。电源还可以包括一个或一个以上的直流或交流电源、再充电装置、电源故障检测电路、电源转换器或者逆变器、电源状态指示器等任意组件。所述电子设备1还可以包括多种传感器、蓝牙模块、Wi-Fi模块等，在此不再赘述。
进一步地，所述电子设备1还可以包括网络接口，可选地，所述网络接口可以包括有线接口和/或无线接口(如WI-FI接口、蓝牙接口等)，通常用于在该电子设备1与其他电子设备之间建立通信连接。
可选地，该电子设备1还可以包括用户接口，用户接口可以是显示器(Display)、输入单元(比如键盘(Keyboard))，可选地，用户接口还可以是标准的有线接口、无线接口。可选地，在一些实施例中，显示器可以是LED显示器、液晶显示器、触控式液晶显示器以及OLED(Organic Light-Emitting Diode，有机发光二极管)触摸器等。其中，显示器也可以适当的称为显示屏或显示单元，用于显示在电子设备1中处理的信息以及用于显示可视化的用户界面。
应该了解，所述实施例仅为说明之用，在专利申请范围上并不受此结构的限制。
所述电子设备1中的所述存储器11存储的损伤图像智能定损程序12是多个指令的组合，在所述处理器10中运行时，可以实现：
获取原始损伤图像集，根据历史损伤图像集对所述原始损伤图像集进行采样，得到采样图像集；
获取所述采样图像集中每个采样图像的所有特征层，并将所述采样图像中每个特征层的尺寸调整为相同尺寸，得到初始损伤图像集；
计算所述初始损伤图像集中每个特征层的语义信息；
利用预设的语义特征强化函数对所述语义信息进行特征强化，得到标准损伤图像集；
计算所述标准损伤图像集中每个特征层的损伤值，根据所述损伤值检测所述标准损伤图像集的损伤位置，并裁剪出所述损伤位置对应的标准损伤图像集中的图像，得到目标损伤图像集；
通过预先训练完成的图像损伤程度检测模型对所述目标损伤图像集进行损伤程度检测，得到所述目标损伤图像集的损伤程度。
具体地，所述处理器10对上述指令的具体实现方法可参考图1对应实施例中相关步骤的描述，在此不赘述。
进一步地，所述电子设备1集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读存储介质中。所述计算机可读存储介质可以包括：能够携带所述计算机程序代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器(ROM，Read-Only Memory)。所述计算机可读存储介质可以是非易失性，也可以是易失性。
进一步地，所述计算机可读存储介质可主要包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需的应用程序等；存储数据区可存储根据区块链节点的使用所创建的数据等。
在本申请所提供的几个实施例中，应该理解到，所揭露的设备，装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述模块的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式。
所述作为分离部件说明的模块可以是或者也可以不是物理上分开的，作为模块显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络 单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能模块可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用硬件加软件功能模块的形式实现。
对于本领域技术人员而言，显然本申请不限于上述示范性实施例的细节，而且在不背离本申请的精神或基本特征的情况下，能够以其他的具体形式实现本申请。
因此，无论从哪一点来看，均应将实施例看作是示范性的，而且是非限制性的，本申请的范围由所附权利要求而不是上述说明限定，因此旨在将落在权利要求的等同要件的含义和范围内的所有变化涵括在本申请内。不应将权利要求中的任何附关联图标记视为限制所涉及的权利要求。
本申请所指区块链是分布式数据存储、点对点传输、共识机制、加密算法等计算机技术的新型应用模式。区块链(Blockchain)，本质上是一个去中心化的数据库，是一串使用密码学方法相关联产生的数据块，每一个数据块中包含了一批次网络交易的信息，用于验证其信息的有效性(防伪)和生成下一个区块。区块链可以包括区块链底层平台、平台产品服务层以及应用服务层等。
此外，显然“包括”一词不排除其他单元或步骤，单数不排除复数。系统权利要求中陈述的多个单元或装置也可以由一个单元或装置通过软件或者硬件来实现。第二等词语用来表示名称，而并不表示任何特定的顺序。
最后应说明的是，以上实施例仅用以说明本申请的技术方案而非限制，尽管参照较佳实施例对本申请进行了详细说明，本领域的普通技术人员应当理解，可以对本申请的技术方案进行修改或等同替换，而不脱离本申请技术方案的精神和范围。

Claims (20)

  1. 一种损伤图像智能定损方法，其中，所述方法包括：
    获取原始损伤图像集，根据历史损伤图像集对所述原始损伤图像集进行采样，得到采样图像集；
    获取所述采样图像集中每个采样图像的所有特征层，将所述采样图像中每个特征层的尺寸调整为相同尺寸，得到初始损伤图像集；
    计算所述初始损伤图像集中每个特征层的语义信息，利用预设的语义特征强化函数对所述语义信息进行特征强化，得到标准损伤图像集；
    计算所述标准损伤图像集中每个特征层的损伤值，根据所述损伤值检测所述标准损伤图像集的损伤位置，并裁剪出所述损伤位置对应的标准损伤图像集中的图像，得到目标损伤图像集；
    通过预先训练完成的图像损伤程度检测模型对所述目标损伤图像集进行损伤程度检测，得到所述目标损伤图像集的损伤程度。
  2. 如权利要求1所述的损伤图像智能定损方法，其中，所述对所述原始损伤图像集进行采样，得到采样图像集，包括：
    计算所述原始损伤图像集与所述原始损伤图像集损伤类别相同的历史损伤图像集的交并比值，其中，所述历史损伤图像集表示预先对图像标记为损伤图像；
    根据所述交并比值筛选出所述原始图像集中的难例图像并剔除，得到所述采样图像集。
  3. 如权利要求2所述的损伤图像智能定损方法，其中，所述根据所述交并比值筛选出所述原始图像集中的难例图像包括：
    若所述交并比值小于预设的交并比阈值时，则将所述交并比值对应的原始损伤图像作为难例图像并剔除；
    若所述交并比值不小于预设的交并比阈值，则将所述交并比值对应的原始损伤图像作为采样图像。
  4. 如权利要求1所述的损伤图像智能定损方法，其中，所述预设的语义特征强化函数包括：
    Figure PCTCN2020098972-appb-100001
    其中，y i表示特征强化后的语义信息权重，x i表示需要进行特征强化的语义信息，x j表示不需要进行特征强化的语义信息权重，w g表示语义信息的偏置，C(x)表示语义信息的归一化参数，e为无限不循环小数。
  5. 如权利要求1所述的损伤图像智能定损方法，其中，所述计算所述标准损伤图像集中每个特征层的损伤值，根据所述损伤值检测所述标准损伤图像集的损伤位置，包括：
    利用预设的损伤函数计算所述标准损伤图像集中每个特征层的损伤值，选取所述损伤值大于预设损伤阈值的特征层，得到损伤特征层，根据所述损伤特征层，检测出所述标准损伤图像集的损伤位置；
    其中，所述预设的损伤函数包括：
    Figure PCTCN2020098972-appb-100002
    γ＝αln(b+1)
    其中，L b(x)表示损伤值，x表示标准损伤图像集的像素值，α和b分别表示标准损伤图像集的权重和偏置，C表示标准损伤图像集的归一化参数。
  6. 如权利要求1至5中任意一项所述的损伤图像智能定损方法，其中，该方法还包 括训练所述图像损伤程度检测模型，其中，所述训练包括：
    获取训练图像集以及所述训练图像集的标签值；
    将所述训练图像集输入至所述图像损伤程度检测模型中进行训练，得到训练值，并通过预设的损失函数计算所述训练值与所述标签值的损失函数值；
    在所述损失函数值大于预设的阈值时，重新调整所述图像损伤程度检测模型中的参数，并利用参数调整后的所述图像损伤程度检测模型重新对所述训练图像集进行训练，直至所述损失函数值不大于所述预设的阈值时，得到训练完成的图像损伤程度检测模型。
  7. 如权利要求6所述的损伤图像智能定损方法，其中，所述损失函数包括：
    Figure PCTCN2020098972-appb-100003
    其中，L(s)表示损失函数值，k表示训练图像集的数量，y i表示训练值，y′ i表示标签值。
  8. 一种电子设备，其中，所述电子设备包括：
    至少一个处理器；以及，
    与所述至少一个处理器通信连接的存储器；其中，
    所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行如下所述的损伤图像智能定损方法：
    获取原始损伤图像集，根据历史损伤图像集对所述原始损伤图像集进行采样，得到采样图像集；
    获取所述采样图像集中每个采样图像的所有特征层，将所述采样图像中每个特征层的尺寸调整为相同尺寸，得到初始损伤图像集；
    计算所述初始损伤图像集中每个特征层的语义信息，利用预设的语义特征强化函数对所述语义信息进行特征强化，得到标准损伤图像集；
    计算所述标准损伤图像集中每个特征层的损伤值，根据所述损伤值检测所述标准损伤图像集的损伤位置，并裁剪出所述损伤位置对应的标准损伤图像集中的图像，得到目标损伤图像集；
    通过预先训练完成的图像损伤程度检测模型对所述目标损伤图像集进行损伤程度检测，得到所述目标损伤图像集的损伤程度。
  9. 如权利要求8所述的电子设备，其中，所述对所述原始损伤图像集进行采样，得到采样图像集，包括：
    计算所述原始损伤图像集与所述原始损伤图像集损伤类别相同的历史损伤图像集的交并比值，其中，所述历史损伤图像集表示预先对图像标记为损伤图像；
    根据所述交并比值筛选出所述原始图像集中的难例图像并剔除，得到所述采样图像集。
  10. 如权利要求8所述的电子设备，其中，所述预设的语义特征强化函数包括：
    Figure PCTCN2020098972-appb-100004
    其中，y i表示特征强化后的语义信息权重，x i表示需要进行特征强化的语义信息，x j表示不需要进行特征强化的语义信息权重，w g表示语义信息的偏置，C(x)表示语义信息的归一化参数，e为无限不循环小数。
  11. 如权利要求8所述的电子设备，其中，所述计算所述标准损伤图像集中每个特征层的损伤值，根据所述损伤值检测所述标准损伤图像集的损伤位置，包括：
    利用预设的损伤函数计算所述标准损伤图像集中每个特征层的损伤值，选取所述损伤值大于预设损伤阈值的特征层，得到损伤特征层，根据所述损伤特征层，检测出所述标准损伤图像集的损伤位置；
    其中，所述预设的损伤函数包括：
    Figure PCTCN2020098972-appb-100005
    γ＝αln(b+1)
    其中，L b(x)表示损伤值，x表示标准损伤图像集的像素值，α和b分别表示标准损伤图像集的权重和偏置，C表示标准损伤图像集的归一化参数。
  12. 如权利要求8至11中任意一项所述的电子设备，其中，该方法还包括训练所述图像损伤程度检测模型，其中，所述训练包括：
    获取训练图像集以及所述训练图像集的标签值；
    将所述训练图像集输入至所述图像损伤程度检测模型中进行训练，得到训练值，并通过预设的损失函数计算所述训练值与所述标签值的损失函数值；
    在所述损失函数值大于预设的阈值时，重新调整所述图像损伤程度检测模型中的参数，并利用参数调整后的所述图像损伤程度检测模型重新对所述训练图像集进行训练，直至所述损失函数值不大于所述预设的阈值时，得到训练完成的图像损伤程度检测模型。
  13. 如权利要求12所述的电子设备，其中，所述损失函数包括：
    Figure PCTCN2020098972-appb-100006
    其中，L(s)表示损失函数值，k表示训练图像集的数量，y i表示训练值，y′ i表示标签值。
  14. 一种计算机可读存储介质，其中，包括存储数据区和存储程序区，存储数据区存储根据区块链节点的使用所创建的数据，存储程序区存储有计算机程序，其中，所述计算机程序被处理器执行时实现如下所述的损伤图像智能定损方法：
    获取原始损伤图像集，根据历史损伤图像集对所述原始损伤图像集进行采样，得到采样图像集；
    获取所述采样图像集中每个采样图像的所有特征层，将所述采样图像中每个特征层的尺寸调整为相同尺寸，得到初始损伤图像集；
    计算所述初始损伤图像集中每个特征层的语义信息，利用预设的语义特征强化函数对所述语义信息进行特征强化，得到标准损伤图像集；
    计算所述标准损伤图像集中每个特征层的损伤值，根据所述损伤值检测所述标准损伤图像集的损伤位置，并裁剪出所述损伤位置对应的标准损伤图像集中的图像，得到目标损伤图像集；
    通过预先训练完成的图像损伤程度检测模型对所述目标损伤图像集进行损伤程度检测，得到所述目标损伤图像集的损伤程度。
  15. 如权利要求14所述的计算机可读存储介质，其中，所述对所述原始损伤图像集进行采样，得到采样图像集，包括：
    计算所述原始损伤图像集与所述原始损伤图像集损伤类别相同的历史损伤图像集的交并比值，其中，所述历史损伤图像集表示预先对图像标记为损伤图像；
    根据所述交并比值筛选出所述原始图像集中的难例图像并剔除，得到所述采样图像集。
  16. 如权利要求14所述的计算机可读存储介质，其中，所述预设的语义特征强化函数包括：
    Figure PCTCN2020098972-appb-100007
    其中，y i表示特征强化后的语义信息权重，x i表示需要进行特征强化的语义信息，x j表示不需要进行特征强化的语义信息权重，w g表示语义信息的偏置，C(x)表示语义信息的归一化参数，e为无限不循环小数。
  17. 如权利要求14所述的计算机可读存储介质，其中，所述计算所述标准损伤图像集中每个特征层的损伤值，根据所述损伤值检测所述标准损伤图像集的损伤位置，包括：
    利用预设的损伤函数计算所述标准损伤图像集中每个特征层的损伤值，选取所述损伤值大于预设损伤阈值的特征层，得到损伤特征层，根据所述损伤特征层，检测出所述标准损伤图像集的损伤位置；
    其中，所述预设的损伤函数包括：
    Figure PCTCN2020098972-appb-100008
    γ＝αln(b+1)
    其中，L b(x)表示损伤值，x表示标准损伤图像集的像素值，α和b分别表示标准损伤图像集的权重和偏置，C表示标准损伤图像集的归一化参数。
  18. 如权利要求14至17中任意一项所述的计算机可读存储介质，其中，该方法还包括训练所述图像损伤程度检测模型，其中，所述训练包括：
    获取训练图像集以及所述训练图像集的标签值；
    将所述训练图像集输入至所述图像损伤程度检测模型中进行训练，得到训练值，并通过预设的损失函数计算所述训练值与所述标签值的损失函数值；
    在所述损失函数值大于预设的阈值时，重新调整所述图像损伤程度检测模型中的参数，并利用参数调整后的所述图像损伤程度检测模型重新对所述训练图像集进行训练，直至所述损失函数值不大于所述预设的阈值时，得到训练完成的图像损伤程度检测模型。
  19. 如权利要求18所述的计算机可读存储介质，其中，所述损失函数包括：
    Figure PCTCN2020098972-appb-100009
    其中，L(s)表示损失函数值，k表示训练图像集的数量，y i表示训练值，y′ i表示标签值。
  20. 一种损伤图像智能定损装置，其中，所述装置包括：
    采样模块，用于获取原始损伤图像集，根据历史损伤图像集对所述原始损伤图像集进行采样，得到采样图像集；
    调整模块，用于获取所述采样图像集中每个采样图像的所有特征层，将所述采样图像中每个特征层的尺寸调整为相同尺寸，得到初始损伤图像集；
    强化模块，用于计算所述初始损伤图像集中每个特征层的语义信息，用于利用预设的语义特征强化函数对所述语义信息进行特征强化，得到标准损伤图像集；
    检测模块，用于计算所述标准损伤图像集中每个特征层的损伤值，根据所述损伤值检测所述标准损伤图像集的损伤位置，并裁剪出所述损伤位置对应的标准损伤图像集中的图像，得到目标损伤图像集；
    定损模块，用于通过预先训练完成的图像损伤程度检测模型对所述目标损伤图像集进行损伤程度检测，得到所述目标损伤图像集的损伤程度。
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106504248A (zh) * 2016-12-06 2017-03-15 成都通甲优博科技有限责任公司 基于计算机视觉的车辆损伤判别方法
CN109657716A (zh) * 2018-12-12 2019-04-19 天津卡达克数据有限公司 一种基于深度学习的车辆外观损伤识别方法
CN110458301A (zh) * 2019-07-11 2019-11-15 深圳壹账通智能科技有限公司 一种车辆部件的定损方法、装置、计算机设备及存储介质

Family Cites Families (6)

* Cited by examiner, † Cited by third party
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CN109191453A (zh) * 2018-09-14 2019-01-11 北京字节跳动网络技术有限公司 用于生成图像类别检测模型的方法和装置
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CN110874594B (zh) * 2019-09-23 2023-06-30 平安科技（深圳）有限公司 基于语义分割网络的人体外表损伤检测方法及相关设备
CN110728236B (zh) * 2019-10-12 2020-12-04 创新奇智(重庆)科技有限公司 车辆定损方法及其专用设备
CN110895814B (zh) * 2019-11-30 2023-04-18 南京工业大学 基于上下文编码网络的航空发动机孔探图像损伤分割方法

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106504248A (zh) * 2016-12-06 2017-03-15 成都通甲优博科技有限责任公司 基于计算机视觉的车辆损伤判别方法
CN109657716A (zh) * 2018-12-12 2019-04-19 天津卡达克数据有限公司 一种基于深度学习的车辆外观损伤识别方法
CN110458301A (zh) * 2019-07-11 2019-11-15 深圳壹账通智能科技有限公司 一种车辆部件的定损方法、装置、计算机设备及存储介质

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
LIU YAHUI, YAO JIAN, LU XIAOHU, XIE RENPING, LI LI: "DeepCrack: A deep hierarchical feature learning architecture for crack segmentation", NEUROCOMPUTING, ELSEVIER, AMSTERDAM, NL, vol. 338, 1 April 2019 (2019-04-01), AMSTERDAM, NL , pages 139 - 153, XP055867123, ISSN: 0925-2312, DOI: 10.1016/j.neucom.2019.01.036 *
MARTIN ATZMUELLER, ALVIN CHIN, FREDERIK JANSSEN, IMMANUEL SCHWEIZER, CHRISTOPH TRATTNER: "ICIAP: International Conference on Image Analysis and Processing, 17th International Conference, Naples, Italy, September 9-13, 2013. Proceedings", vol. 11214 Chap.17, 6 October 2018, SPRINGER , Berlin, Heidelberg , ISBN: 978-3-642-17318-9, article ZHANG ZHENLI; ZHANG XIANGYU; PENG CHAO; XUE XIANGYANG; SUN JIAN: "ExFuse: Enhancing Feature Fusion for Semantic Segmentation", pages: 273 - 288, XP047488335, 032548, DOI: 10.1007/978-3-030-01249-6_17 *

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