CN111985499A - High-precision bridge apparent disease identification method based on computer vision - Google Patents
High-precision bridge apparent disease identification method based on computer vision Download PDFInfo
- Publication number
- CN111985499A CN111985499A CN202010717371.7A CN202010717371A CN111985499A CN 111985499 A CN111985499 A CN 111985499A CN 202010717371 A CN202010717371 A CN 202010717371A CN 111985499 A CN111985499 A CN 111985499A
- Authority
- CN
- China
- Prior art keywords
- disease
- image
- training
- training set
- yolo
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/56—Extraction of image or video features relating to colour
Abstract
The invention discloses a high-precision bridge apparent disease identification method based on computer vision, which comprises the following steps: the method comprises an image preprocessing stage, a simulation image generation stage and an apparent disease identification stage. In the image preprocessing stage, Gaussian filtering and image pixel equalization processing are carried out on a training set containing a disease image. In the generation stage of the simulation image, a data distribution mode for generating a confrontation network learning training set is adopted, and the simulation image is generated according to the data distribution mode, so that the data scale of the training set is increased. In the stage of disease identification, an increased disease training set is adopted to train a YOLO model, and the trained model is used for bridge apparent disease identification. When the training data set is small in scale, the scale of the neural network training data set is enlarged by generating the confrontation network, and the accuracy of apparent disease identification of the YOLO model is guaranteed.
Description
Technical Field
The invention relates to a high-precision bridge apparent disease identification method based on computer vision, which is suitable for the field of health monitoring of civil traffic structures, optimally extracts a disease target boundary of a training image, increases a training set of a neural network model, and improves the identification precision of the identification model on a disease target.
Technical Field
The rapid development of the civil transportation industry, a large number of capital construction facilities enter the operation and maintenance stage. Along with the increase of service time, the appearance of an engineering structure is continuously changed, such as steel corrosion, concrete cracks, steel structure node bolt falling and the like. These appearance defects directly or indirectly affect the change of the mechanical properties of the structure, and the durability and even the safety of the structure may be reduced to some extent, so that the monitoring of the appearance defects of the structure is increasingly important and becomes an important part of the monitoring of the health of the structure. In the aspect of appearance disease monitoring, the traditional mode mainly based on manual detection is limited by the development of structure height and span, and has the characteristics of difficult accessibility and huge workload. In recent years, the development of computer vision and deep learning concepts makes the acquisition, processing and recognition of images tend to be automated and intelligent, the accessibility is strong, the accuracy is high, and classic methods such as RCNN, Fast RCNN, SSD, YOLO and the like appear, wherein YOLO is one of the most advanced target detection schemes so far. Therefore, structural health monitoring means based on computer vision and deep learning have been developed greatly.
At the present stage, the apparent disease identification based on computer vision and deep learning algorithm has certain development bottleneck and faces various difficulties: (1) due to the reasons of acquisition equipment, working environment, shooting angle and the like, the definition of the acquired image information is not enough, so that the accuracy of image target identification is severely restricted; (2) the image recognition by applying the deep learning is based on a large amount of training data sets, and in order to ensure the accurate recognition of targets in different environments and different states, a large amount of original samples are required to be provided as training sets, so that the balance of the training sets is ensured, and the accuracy of the target recognition is improved; (3) the actual engineering environment is complex and changeable, the influence factors are numerous, and how to eliminate the key problem in the image target identification of the same interference components in the obtained image is solved; (4) for the most interesting disease problem in the engineering structure, the boundary marking needs to be carried out on common diseases, samples with high precision as much as possible are provided for deep learning, and therefore the accuracy of recognizing the diseases by the trained model is improved.
The generation of the countermeasure network is taken as a deep learning method which is started in recent years, through mutual game between a generator and a discriminator, semi-supervised learning can be realized by a small amount of marked training samples, so that a high-precision simulation image is generated, and the problem of acquisition of a large amount of training data sets can be solved. Meanwhile, by means of an image processing technology of computer vision, a training set with high precision, accurate marking and wide coverage range is generated for screening and processing the existing engineering structure disease sample images. The method has the advantages that the YOLO neural network model with high precision and fast operation is trained to obtain the model for accurately identifying the surface diseases, and the method has important application values for guaranteeing the safe service of the infrastructure structure and reducing irreversible damage of the structure caused by missing detection of the appearance diseases.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, a high-precision bridge apparent disease identification method based on computer vision is provided, the definition of a disease image training set is improved, the disease target boundary of the training set is optimally extracted, the number of the training sets is increased, and the disease identification precision of a deep learning model is improved.
The technical scheme is as follows: in order to achieve the purpose, the technical scheme of the invention is as follows:
a high-precision bridge apparent disease identification method based on computer vision comprises the following steps:
the first step is as follows: and (5) an image preprocessing stage.
And carrying out Gaussian filtering and image pixel equalization on the disease images in the disease image training set, wherein local abnormal noise points are removed by the Gaussian filtering, pixels with smaller occupation ratio are merged by the image pixel equalization, the pixel gray distribution distance is effectively increased, and the definition of the input training images is improved.
The second step is that: simulation image generation phase
And training the established generated countermeasure network by utilizing the preprocessed disease image training set, wherein the generated countermeasure network consists of a generator and a discriminator, the training discriminator discriminates whether the image is true or false, and the training generator generates a simulation image. Firstly fixing the generator and the training discriminator, and then fixing the discriminator and the training generator. When the discriminator judges that the probability distribution of the generated image of the generator is consistent with the probability distribution of the training set and the frequency domain distribution is consistent, the generator is determined to generate a simulated disease image set; otherwise, the generator continues to be trained.
The third step: apparent disease identification stage
(1) Optimizing the disease boundary labels of the preprocessed disease image training set and the simulated disease image set: performing frame selection on a disease target position in the disease image through artificial vision to serve as an initial label; in the frame selection range, Canny operators and Sobel operators are jointly used for automatically optimizing and identifying the disease boundaries to serve as training labels. The disease image training set and the simulated disease image which are preprocessed with the training labels form a YOLO sample set, and the YOLO sample set is respectively a YOLO training set and a YOLO testing set.
(2) And inputting the YOLO training set into a YOLO neural network model for training. After training is finished, the identification precision of the disease target is identified by using the test identification model, if the identification precision meets a threshold value of correct identification rate, the disease target is considered to be qualified, and the currently trained YOLO neural network model is the disease identification model; otherwise, supplementing the disease image, and repeating the first step to the third step until the correct recognition rate meets the threshold requirement.
Further, the gaussian filtering step is as follows:
the first step is as follows: converting the disease images in the disease image training set into an HSV color model, wherein H is hue, S is saturation, and V is brightness;
the second step is that: performing convolution calculation on the H channel, the S channel and the V channel by adopting a Gaussian convolution kernel;
the third step: adjusting according to the actual needs of the original disease image saturation and lightness, and changing the corresponding weight coefficient; and converting the disease image into an RGB color model through a conversion formula.
Further, the image equalization step is as follows:
the first step is as follows: converting the RGB disease image after Gaussian filtering into a gray image;
the second step is that: counting a grey image histogram, and grouping and merging grey values according to a probability density function;
the third step: and (4) taking the gray value ratio of the pixels before and after merging as an equalization coefficient, and multiplying the equalization coefficient by the RGB three-channel colors at the corresponding pixel point positions respectively to reconstruct the disease image.
Furthermore, the Sobel operator obtains an optimal disease boundary by using a difference method on the basis that the Canny operator identifies the disease boundary, and the optimal disease boundary is used as a training label.
The invention has the beneficial effects that:
(1) the disease image training set is preprocessed, wherein the preprocessing comprises Gaussian filtering and image pixel equalization, local abnormal noise points of the image can be effectively removed, pixels are merged, and the image definition of the training set is greatly improved;
(2) by jointly using the characteristic of a multistage edge detection algorithm of a Canny operator and the characteristic of a discrete difference algorithm of a Sobel operator, the image disease boundary is optimally extracted, and the accuracy of a weight coefficient in the training process of the YOLO neural network model is realized;
(3) the invention utilizes the image simulation performance of the anti-generation network to train the generator to generate a large number of simulated disease images, solves the problem of insufficient disease image data set in the training process, thereby effectively improving the disease identification precision after the neural network model is trained and having good application prospect.
Drawings
FIG. 1 is a flow chart of a high-precision bridge apparent disease identification method based on computer vision;
FIG. 2 is a flow chart of Gaussian filtering during image pre-processing;
FIG. 3 is a flow chart of image pixel equalization during an image pre-processing phase;
FIG. 4 is a diagram of generator training termination conditions;
Detailed Description
The technical solution of the present invention will be further described in detail with reference to the accompanying drawings.
As shown in fig. 1, the implementation process of the method of the present invention is described in detail by taking the high-strength bolt fall-off identification of a certain large-span steel truss bridge node as an example, and the method mainly comprises the following steps:
(1) and (5) preprocessing the node bolt falling image training set. The method includes the steps that a plurality of steel truss bridge node images with high-strength bolt falling diseases are collected together by considering influence factors such as shooting distance, lighting conditions and visual angles, Gaussian filtering and image pixel equalization are conducted on a bolt falling image color model, local abnormal noise points are removed through the Gaussian filtering, pixels with small occupation ratios are merged through the image equalization, pixel distribution distances are effectively expanded, and input training image definition is improved;
(2) and training a set of training discriminators by using the node bolt falling image to discriminate the truth of the generated image. The nature of the discriminator is a two-classifier, and for a 'new sample' formed by fusing a real high-strength bolt damage sample library x and a generation sample library z, the discriminator is used for Pdata(x) And Pz(z) and the probability score difference, and the generated defective sample of the primary screening, namely the training target can be expressed as:
wherein D is a target value obtained by optimizing the discriminator D and the generator G,
for a sample pool Pdata(x) Is a desired value of
For generating a sample pool PzThe expected value of (z), D (x) is the output value of the discriminator D to the sample pool x, and D (G (z)) is the output value of the discriminator D to the generator G as input.
(3) And (5) training the generator by using the node bolt falling image training set. The generator continuously evolves and generates images closer to real samples in the training process, and the difference between the generated bolt falling images and the real bolt images is reduced. When the discriminator discriminates that the probability density distribution and the frequency domain distribution of the generated image of the generator are consistent with those of the training set, determining the generator as shown in FIG. 4, and generating a certain amount of simulation bolt falling images; otherwise, the generator continues to be trained.
(4) And optimizing the boundary labels of the training set of the node bolt falling image and the simulation bolt falling image. Firstly, coordinates and categories of a bounding box with bolt falling holes in high-strength bolt nodes on the images in the training set are marked through artificial vision, and the coordinates and the categories are used as initial labels. Then, in the frame selection range, automatically identifying the falling boundary by jointly using a Canny operator and a Sobel operator to form the boundary of the falling bolt hole as a training label for bolt falling;
(5) and inputting the bolt shedding training set with the training label into a YOLO neural network model for training. After training is finished, a certain amount of verification data sets are selected, the recognition accuracy of the recognition model on the disease target is tested, and the correct recognition rate threshold is set to be 95%. If the correct recognition rate is over 95%, the trained recognition model is considered to be qualified; and otherwise, supplementing the bolt falling image of the complementary shooting node, repeating the work of (1) to (5) until the correct recognition rate meets the threshold requirement, finishing the training of the recognition model, and meeting the precision requirement.
As shown in fig. 2, the principle of gaussian filtering on the bolt-off image training set is to convert the images in the training set into HSV color models, where H channel represents hue, S channel represents saturation, and V channel represents lightness; and respectively carrying out convolution calculation on the H channel, the S channel and the V channel by adopting Gaussian convolution kernels with different standard deviations according to needs, adjusting the weights of the S channel and the V channel according to different characteristics of bolt falling and combined with actual recognition needs, converting the adjusted three channels back to an RGB color model, and generating a filtered high-strength bolt falling training set image.
As shown in fig. 3, the image pixel equalization needs to convert the bolt shedding training set image after gaussian filtering into a gray image, and the gray value calculation formula of each pixel point is as follows:
Gray＝R*0.299+G*0.587+B*0.114 (1)
wherein, the RGB image is a true color image, R, G, B represents 3 different basic colors of red, green and blue respectively, and Gray is a Gray value.
Counting a grey image histogram, and grouping and merging grey values according to a probability density function; and taking the ratio of the gray value of the pixel point after merging to the gray value of the pixel point before merging as an equalization coefficient, and multiplying the equalization coefficient by the RGB three-channel colors at the corresponding pixel point position respectively to reconstruct the image.
As shown in fig. 4, is a generator training termination condition diagram. Converting the simulated disease image generated by the generator and the image of the training set into a gray image, counting a gray distribution histogram, and fitting a probability density curve; meanwhile, the images of the simulated disease images generated by the generator and the images of the training set are subjected to Fourier transform to frequency domains, and the distribution rules of the frequency domains are compared. If the probability density distribution and the frequency domain distribution are consistent, the generator determines that a simulated disease image can be generated and used as a training set for training a YOLO neural network model.
The above description is only the preferred embodiment of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications can be made without departing from the principles of the invention and these modifications are to be considered within the scope of the invention.
Claims (4)
1. A high-precision bridge apparent disease identification method based on computer vision is characterized by comprising the following steps:
the first step is as follows: image preprocessing stage
Carrying out Gaussian filtering and image pixel equalization on the disease images in the disease image training set to complete the pretreatment of the disease image training set;
the second step is that: simulation image generation phase
Training the established generation countermeasure network by utilizing the preprocessed disease image training set, wherein the generation countermeasure network consists of a generator and a discriminator; during training, firstly fixing the generator and the training discriminator, then fixing the discriminator and the training generator, when the probability distribution of the image generated by the discriminator discrimination generator and the probability distribution of the preprocessed disease image training set are consistent and the frequency domain distribution is consistent, determining the generator, generating a simulated disease image set, and otherwise, continuing to train the generator;
the third step: apparent disease identification stage
(1) Respectively performing frame selection on disease target positions of the disease images in the preprocessed disease image training set and the disease images in the simulated disease image set through artificial vision to serve as initial labels; in the frame selection range, a Canny operator and a Sobel operator are jointly used for automatically optimizing and identifying the disease boundary to be used as a training label; forming a YOLO sample set by the preprocessed disease image training set with training labels and the simulated disease images, and respectively setting the YOLO sample set as a YOLO training set and a YOLO testing set;
(2) training a YOLO neural network model by using a YOLO training set, testing the trained YOLO neural network model by using a YOLO testing set, and if the correct recognition rate of the trained YOLO neural network model on a disease target is greater than a set threshold value, determining the currently trained YOLO neural network model as a disease recognition model; otherwise, supplementing the disease images in the disease image training set, and repeating the first step to the third step until the correct recognition rate is greater than the set threshold value.
2. The method for identifying the apparent bridge diseases based on the computer vision is characterized in that in the first step, the Gaussian filtering step is as follows:
the first step is as follows: converting the disease images in the disease image training set from an RGB color model into an HSV color model, wherein H is hue, S is saturation and V is lightness;
the second step is that: performing convolution calculation on the H channel, the S channel and the V channel by adopting a Gaussian convolution kernel;
the third step: adjusting the weighting coefficients of an S channel and a V channel according to the actual needs of the saturation and lightness of the original disease image; and converting the disease image of the HSV color model into an RGB color model through a conversion formula.
3. The method for identifying the apparent bridge diseases based on the computer vision is characterized in that in the first step, the image equalization step is as follows:
the first step is as follows: converting the disease image after Gaussian filtering into a gray image;
the second step is that: counting a grey image histogram, and grouping and merging grey values according to a probability density function;
the third step: and (4) taking the gray value ratio of the pixels before and after merging as an equalization coefficient, and multiplying the equalization coefficient by the RGB three-channel colors at the corresponding pixel point positions respectively to reconstruct the disease image.
4. The method for identifying the apparent diseases of the bridge with high precision based on the computer vision of claim 1, wherein in the third step, the Canny operator and the Sobel operator are jointly applied to automatically optimize and identify the disease boundaries specifically as follows: the Sobel operator obtains the optimal disease boundary by using a difference method on the basis that the Canny operator identifies the disease boundary, and the optimal disease boundary is used as a training label.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010717371.7A CN111985499B (en) | 2020-07-23 | 2020-07-23 | High-precision bridge apparent disease identification method based on computer vision |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010717371.7A CN111985499B (en) | 2020-07-23 | 2020-07-23 | High-precision bridge apparent disease identification method based on computer vision |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111985499A true CN111985499A (en) | 2020-11-24 |
| CN111985499B CN111985499B (en) | 2022-11-04 |
Family
ID=73438856
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010717371.7A Active CN111985499B (en) | 2020-07-23 | 2020-07-23 | High-precision bridge apparent disease identification method based on computer vision |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111985499B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112613097A (en) * | 2020-12-15 | 2021-04-06 | 中铁二十四局集团江苏工程有限公司 | BIM rapid modeling method based on computer vision |
| CN113112498A (en) * | 2021-05-06 | 2021-07-13 | 东北农业大学 | Grape leaf scab identification method based on fine-grained countermeasure generation network |
| CN113313107A (en) * | 2021-04-25 | 2021-08-27 | 湖南桥康智能科技有限公司 | Intelligent detection and identification method for multiple types of diseases on cable surface of cable-stayed bridge |
| CN113627299A (en) * | 2021-07-30 | 2021-11-09 | 广东电网有限责任公司 | Intelligent wire floater identification method and device based on deep learning |
| CN116682072A (en) * | 2023-08-04 | 2023-09-01 | 四川公路工程咨询监理有限公司 | Bridge disease monitoring system |
| CN117067226A (en) * | 2023-08-17 | 2023-11-17 | 兰州交通大学 | Steel bridge rust detection robot and rust detection method |
| CN117292248A (en) * | 2023-10-30 | 2023-12-26 | 吉林农业大学 | Deep learning-based farmland pesticide spraying system and weed detection method |
| CN117292248B (en) * | 2023-10-30 | 2024-04-26 | 吉林农业大学 | Deep learning-based farmland pesticide spraying system and weed detection method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110188824A (en) * | 2019-05-31 | 2019-08-30 | 重庆大学 | A kind of small sample plant disease recognition methods and system |
| CN111191714A (en) * | 2019-12-28 | 2020-05-22 | 浙江大学 | Intelligent identification method for bridge appearance damage diseases |
| CN111382601A (en) * | 2018-12-28 | 2020-07-07 | 河南中原大数据研究院有限公司 | Illumination face image recognition preprocessing system and method for generating confrontation network model |
-
2020
- 2020-07-23 CN CN202010717371.7A patent/CN111985499B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111382601A (en) * | 2018-12-28 | 2020-07-07 | 河南中原大数据研究院有限公司 | Illumination face image recognition preprocessing system and method for generating confrontation network model |
| CN110188824A (en) * | 2019-05-31 | 2019-08-30 | 重庆大学 | A kind of small sample plant disease recognition methods and system |
| CN111191714A (en) * | 2019-12-28 | 2020-05-22 | 浙江大学 | Intelligent identification method for bridge appearance damage diseases |
Non-Patent Citations (2)
| Title |
|---|
| 刘恋秋: "基于深度卷积生成对抗网络的图像识别算法", 《液晶与显示》 * |
| 韩晓健等: "卷积神经网络在桥梁结构表面病害检测中的应用研究", 《结构工程师》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112613097A (en) * | 2020-12-15 | 2021-04-06 | 中铁二十四局集团江苏工程有限公司 | BIM rapid modeling method based on computer vision |
| CN113313107A (en) * | 2021-04-25 | 2021-08-27 | 湖南桥康智能科技有限公司 | Intelligent detection and identification method for multiple types of diseases on cable surface of cable-stayed bridge |
| CN113112498A (en) * | 2021-05-06 | 2021-07-13 | 东北农业大学 | Grape leaf scab identification method based on fine-grained countermeasure generation network |
| CN113112498B (en) * | 2021-05-06 | 2024-01-19 | 东北农业大学 | Grape leaf spot identification method based on fine-grained countermeasure generation network |
| CN113627299A (en) * | 2021-07-30 | 2021-11-09 | 广东电网有限责任公司 | Intelligent wire floater identification method and device based on deep learning |
| CN113627299B (en) * | 2021-07-30 | 2024-04-09 | 广东电网有限责任公司 | Wire floater intelligent recognition method and device based on deep learning |
| CN116682072A (en) * | 2023-08-04 | 2023-09-01 | 四川公路工程咨询监理有限公司 | Bridge disease monitoring system |
| CN116682072B (en) * | 2023-08-04 | 2023-10-20 | 四川公路工程咨询监理有限公司 | Bridge disease monitoring system |
| CN117067226A (en) * | 2023-08-17 | 2023-11-17 | 兰州交通大学 | Steel bridge rust detection robot and rust detection method |
| CN117292248A (en) * | 2023-10-30 | 2023-12-26 | 吉林农业大学 | Deep learning-based farmland pesticide spraying system and weed detection method |
| CN117292248B (en) * | 2023-10-30 | 2024-04-26 | 吉林农业大学 | Deep learning-based farmland pesticide spraying system and weed detection method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111985499B (en) | 2022-11-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111985499B (en) | High-precision bridge apparent disease identification method based on computer vision | |
| CN110543878B (en) | Pointer instrument reading identification method based on neural network | |
| CN109118479B (en) | Capsule network-based insulator defect identification and positioning device and method | |
| CN109615611B (en) | Inspection image-based insulator self-explosion defect detection method | |
| CN109840471A (en) | A kind of connecting way dividing method based on improvement Unet network model | |
| CN108021938A (en) | A kind of Cold-strip Steel Surface defect online detection method and detecting system | |
| CN110148162A (en) | A kind of heterologous image matching method based on composition operators | |
| CN105975913B (en) | Road network extraction method based on adaptive cluster learning | |
| CN113409314A (en) | Unmanned aerial vehicle visual detection and evaluation method and system for corrosion of high-altitude steel structure | |
| CN109214298A (en) | A kind of Asia women face value Rating Model method based on depth convolutional network | |
| CN111091544A (en) | Method for detecting breakage fault of side integrated framework of railway wagon bogie | |
| CN108681689B (en) | Frame rate enhanced gait recognition method and device based on generation of confrontation network | |
| CN114092697B (en) | Building facade semantic segmentation method with attention fused with global and local depth features | |
| CN111311702B (en) | Image generation and identification module and method based on BlockGAN | |
| CN111260645B (en) | Tampered image detection method and system based on block classification deep learning | |
| CN106951863B (en) | Method for detecting change of infrared image of substation equipment based on random forest | |
| CN110909657A (en) | Method for identifying apparent tunnel disease image | |
| CN112597798A (en) | Method for identifying authenticity of commodity by using neural network | |
| CN116029979A (en) | Cloth flaw visual detection method based on improved Yolov4 | |
| CN113421223B (en) | Industrial product surface defect detection method based on deep learning and Gaussian mixture | |
| CN109472790A (en) | A kind of machine components defect inspection method and system | |
| CN114155226A (en) | Micro defect edge calculation method | |
| CN111310899B (en) | Power defect identification method based on symbiotic relation and small sample learning | |
| CN115830302B (en) | Multi-scale feature extraction fusion power distribution network equipment positioning identification method | |
| CN115082713B (en) | Method, system and equipment for extracting target detection frame by introducing space contrast information |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |