CN111507958A - Target detection method, training method of detection model and electronic equipment - Google Patents
Target detection method, training method of detection model and electronic equipment Download PDFInfo
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- 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
- G06T7/0004—Industrial image inspection
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- 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
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- 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]
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- 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
- G06T2207/30108—Industrial image inspection
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention relates to the technical field of image processing, in particular to a target detection method, a training method of a detection model and electronic equipment, wherein the detection method comprises the steps of obtaining an image to be detected; inputting an image to be detected into a detection model to obtain position information of a target candidate region; the target candidate region is a candidate region of which the sizes of all candidate regions output by the detection model are smaller than a preset value; extracting an image with a preset size from the image to be detected based on the position information of the target candidate area to obtain a sub-image to be detected; and inputting the sub-image to be detected into the detection model to obtain the category of the target corresponding to the target candidate area. According to the detection method, only the candidate region with the size smaller than the preset size is extracted from the image to be detected for secondary detection, so that the data processing amount can be reduced, and the target detection efficiency can be improved.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a target detection method, a training method of a detection model and electronic equipment.
Background
The detection method of the target in the image can be manually detected, but the detection method is influenced by artificial subjective factors, so that the detection accuracy is low. Based on this, a method for automatically detecting an image is also proposed in the prior art to automatically detect an object in the image. However, when there are multiple-size targets in the image to be detected, in order to accurately detect the small-size targets, a method of detecting the small-size targets by dividing the image to be detected into a plurality of smaller sub-pictures is often used.
Taking transmission line detection as an example, transmission line inspection is one of the important works for operating and maintaining a transmission network. At present, the mode of combining helicopter routing inspection, unmanned aerial vehicle routing inspection and robot routing inspection is adopted for power transmission line routing inspection in China, and an artificial intelligent method is used for identifying the acquired power transmission line routing inspection image so as to greatly increase the efficiency of checking the routing inspection image and reduce the workload of routing inspection workers. However, there are many targets with small sizes in the power transmission line inspection picture, and most of the prior art identifies the targets with small sizes in a manner of directly scaling the original picture to a larger resolution or uniformly dividing the original picture into a plurality of smaller pictures. Although these techniques can accurately identify a small-sized target, the method of using a larger-resolution picture or directly cutting an original image and then detecting the original image consumes more computing resources, which results in low target detection efficiency.
Disclosure of Invention
In view of this, embodiments of the present invention provide a target detection method, a training method of a detection model, and an electronic device, so as to solve the problem of low target detection efficiency.
According to a first aspect, an embodiment of the present invention provides a target detection method, including:
acquiring an image to be detected;
inputting the image to be detected into a detection model to obtain the position information of a target candidate region; the target candidate region is a candidate region of which the sizes of all candidate regions output by the detection model are smaller than a preset value;
extracting an image with a preset size from the image to be detected based on the position information of the target candidate area to obtain a sub-image to be detected;
and inputting the sub-image to be detected into the detection model to obtain the category of the target corresponding to the target candidate area.
According to the target detection method provided by the embodiment of the invention, after the position information of the target candidate region is obtained by using the detection model, the image with the preset size is extracted from the image to be detected to obtain the subimage to be detected, and the detection model is used for detecting the subimage to be detected again; that is, only the candidate region having the size smaller than the preset size is extracted from the image to be detected and the detection is performed again, so that the data processing amount can be reduced and the target detection efficiency can be improved.
With reference to the first aspect, in a first implementation manner of the first aspect, the inputting the image to be detected into a detection model to obtain position information of the target candidate region includes:
predicting candidate areas corresponding to all targets in the image to be detected by using a candidate area prediction structure in the detection model;
judging whether the size of the candidate region is smaller than the preset value by using a region judgment structure in the detection model;
and when the size of the candidate region is smaller than the preset value, predicting first position information of the candidate region by using a first region prediction structure in the detection model to obtain the position information of the target candidate region.
According to the target detection method provided by the embodiment of the invention, the size of the candidate region is judged by using the region judgment structure, and when the size is smaller than the preset value, the first position information of the candidate region is predicted only by using the first region preset structure without predicting the category, so that the target detection efficiency can be improved on the premise of ensuring the detection accuracy.
With reference to the first implementation manner of the first aspect, in a second implementation manner of the first aspect, the first location information is center location information of the target candidate region.
According to the target detection method provided by the embodiment of the invention, as the other position information of the target candidate region is obtained by calculating the center position information of the target candidate region, the center position information of the target candidate region is selected as the first position information, so that the data processing amount can be reduced, and the target detection efficiency can be improved.
With reference to the first implementation manner of the first aspect, in a third implementation manner of the first aspect, the inputting the image to be detected into a detection model to obtain position information of the target candidate region further includes:
and when the size of the candidate region is larger than or equal to the preset value, predicting the category and the second position information of the target corresponding to the candidate region by using a second region prediction structure in the detection model.
According to the target detection method provided by the embodiment of the invention, when the size of the candidate region is greater than or equal to the preset value, the second region in the detection model is used for predicting the type and the second position information of the target corresponding to the structural candidate region, and the detection method can ensure the detection efficiency of the small-size target on the basis of ensuring the detection accuracy of the large-size target.
With reference to the first aspect, or any one of the first to third embodiments of the first aspect, in a fourth embodiment of the first aspect, the inputting the image to be detected into a detection model to obtain position information of the target candidate region further includes:
zooming the image to be detected to an image to be detected with a preset resolution;
and inputting the image to be detected with the preset resolution into the detection model to obtain the position information of the target candidate region.
According to the target detection method provided by the embodiment of the invention, before the image to be detected is detected, the image to be detected is zoomed to the image to be detected with the preset resolution ratio, so that the detection accuracy is improved.
With reference to the fourth implementation manner of the first aspect, in the fifth implementation manner of the first aspect, the inputting the sub-image to be detected into the detection model to obtain the category of the target corresponding to the target candidate region includes:
zooming the sub-image to be detected to the sub-image to be detected with the preset resolution;
and inputting the sub-image to be detected with the preset resolution into the detection model to obtain the category of the target corresponding to the target candidate area.
With reference to the first aspect, in a sixth implementation manner of the first aspect, the image to be detected is an image of a power transmission line.
According to the target detection method provided by the embodiment of the invention, as the image of the power transmission line comprises the targets with multiple sizes, the detection method is adopted for carrying out target detection on the image of the power transmission line, only the region possibly containing the small-size targets is detected with finer granularity, and the identification of multiple scale defects is supported, so that the problem of overlarge calculated amount caused by the fact that the existing detection method for the image defects of the power transmission line directly detects the images with large resolution or the images after being partitioned is solved, the calculated amount of image identification is greatly reduced under the condition that the detection precision is not reduced, and the detection efficiency is improved.
According to a second aspect, an embodiment of the present invention further provides a training method for a detection model, including:
acquiring a sample image with marking information; the labeling information is the category corresponding to each target in the sample image and the position information of a target area, and the size of the target area is smaller than a preset value;
inputting the sample image into an initial detection model to obtain the position information of a prediction region;
and updating parameters in the initial detection model based on the position information of the target area marked in the sample image and the position information of the prediction area to obtain the detection model.
According to the training method of the detection model, the detection model is used for predicting the small-size target, the accuracy of small-size target detection can be guaranteed, and accurate guarantee is provided for the follow-up detection of the small-size target by using the detection model.
According to a third aspect, an embodiment of the present invention provides an electronic device, including: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory having stored therein computer instructions, and the processor executing the computer instructions to perform the object detection method according to the first aspect or any one of the embodiments of the first aspect, or to perform the training method of the detection model according to the second aspect.
According to a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the object detection method described in the first aspect or any one of the implementation manners of the first aspect, or execute the training method of the detection model described in the second aspect.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a method of target detection according to an embodiment of the invention;
FIG. 2 is a flow chart of a method of target detection according to an embodiment of the invention;
FIG. 3 is a flow chart of a method of object detection according to an embodiment of the invention;
4a-4c are schematic diagrams of a target detection method according to an embodiment of the invention;
FIG. 5 is a flow chart of a method of training a detection model according to an embodiment of the invention;
FIG. 6 is a block diagram of the structure of an object detection apparatus according to an embodiment of the present invention;
FIG. 7 is a block diagram of an apparatus for training a test pattern according to an embodiment of the present invention;
fig. 8 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the target detection method described in the embodiment of the present invention may be used in various scenarios, for example: power transmission lines, video scenes, etc., and the specific application scenarios are not limited in any way herein. The target may be an object in the image to be detected, or a defect of the object in the image to be detected, or the like.
When the target detection method is applied to the power transmission line, because the image of the power transmission line comprises the targets with multiple sizes, the detection method is adopted for carrying out target detection on the image of the power transmission line, only the region possibly containing the targets with small sizes is detected with finer granularity, and the identification of the defects with multiple sizes is supported, so that the problem of overlarge calculated amount caused by the fact that the existing image defect detection method of the power transmission line directly detects the images with large resolution or the images after being partitioned is solved, the calculated amount of image identification is greatly reduced under the condition that the detection precision is not reduced, and the detection efficiency is improved.
In accordance with an embodiment of the present invention, there is provided an object detection method embodiment, it should be noted that the steps illustrated in the flowchart of the figure may be performed in a computer system such as a set of computer executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
In this embodiment, a target detection method is provided, which can be used in electronic devices, such as a mobile phone, a tablet computer, a computer, and the like, and fig. 1 is a flowchart of the target detection method according to an embodiment of the present invention, as shown in fig. 1, the flowchart includes the following steps:
and S11, acquiring an image to be detected.
The image to be detected can be stored in the electronic device, or can be acquired by the electronic device from the outside, for example, when the target detection method is applied to the power transmission line, the image to be detected can be an original image shot by a camera carried by inspection equipment such as an unmanned aerial vehicle, a helicopter and a robot.
And S12, inputting the image to be detected into the detection model to obtain the position information of the target candidate area.
And the target candidate region is a candidate region of which the sizes of all the candidate regions output by the detection model are smaller than a preset value.
After the electronic device acquires the model to be detected, the image to be detected can be directly input into the detection model, or the image to be detected can be input into the detection model after being preprocessed, and the like.
The detection model may be a model based on fast R-CNN, a model based on SSD, or other classification detection models, and the specific form of the detection model is not limited.
The electronic equipment inputs the image to be detected into the detection model, and the detection model automatically processes the image to be detected to obtain the position information of the target candidate area. The target candidate region is a candidate region corresponding to a small-sized target, wherein the small-sized target is determined by the size of the candidate region corresponding to each target.
The size of the candidate region may be determined by using the number of pixels corresponding to each side length of the candidate region or the area of the candidate region, and may be set according to the actual situation, which is not limited herein.
And S13, extracting an image with a preset size from the image to be detected based on the position information of the target candidate area to obtain a sub-image to be detected.
After obtaining the position information of the target candidate region in S12, the electronic device may correspond the position information to the image to be detected, and extract an image with a preset size from the image to be detected to obtain a sub-image to be detected.
For example, the image to be detected input into the detection model is an original image, and then the electronic device can directly extract an image with a preset size from the image to be detected after obtaining the position information of the target area; if the image to be detected in the input value detection model is an image subjected to scaling processing, the electronic device can obtain the position information corresponding to the image to be detected after obtaining the position information of the target area in the image subjected to scaling processing and processing the position information, and the sub-image to be detected can be extracted from the image to be detected by utilizing the corresponding position information.
And S14, inputting the sub-image to be detected into the detection model to obtain the target category corresponding to the target candidate area.
After obtaining the sub-image to be detected in S13, the electronic device inputs the sub-image to be detected into the detection model, and performs secondary detection on the sub-image to be detected. If the image to be detected input into the detection model in the electronic device S12 is an image subjected to scaling processing, the corresponding processing needs to be performed on the sub-image to be detected before the sub-image to be detected is input into the detection model.
For example, if the image to be detected is scaled to 512 × 512 pixels before the image to be detected is input into the detection model, after the electronic device extracts the sub-image to be detected from the image to be detected, the sub-image to be detected also needs to be scaled to 512 × 512 pixels and then input into the detection model.
It should be noted that if the input of the image to be detected into the detection model still can output the position information of the target candidate region, S13-S14 are required to be executed until all the targets in the image to be detected are detected. That is, the electronic apparatus loops S12-S14 until all objects in the image to be detected are detected.
In the target detection method provided by this embodiment, after the position information of the target candidate region is obtained by using the detection model, an image with a preset size is extracted from the image to be detected to obtain a sub-image to be detected, and the detection model is used to detect the sub-image to be detected again; that is, only the candidate region having the size smaller than the preset size is extracted from the image to be detected and the detection is performed again, so that the data processing amount can be reduced and the target detection efficiency can be improved.
In this embodiment, a target detection method is provided, which can be used in electronic devices, such as a mobile phone, a tablet computer, a computer, etc., fig. 2 is a flowchart of the target detection method according to the embodiment of the present invention, and as shown in fig. 2, the flowchart includes the following steps:
and S21, acquiring an image to be detected.
Please refer to S11 in detail in the embodiment shown in fig. 1, which is not limited herein.
And S22, inputting the image to be detected into the detection model to obtain the position information of the target candidate area.
And the target candidate region is a candidate region of which the sizes of all the candidate regions output by the detection model are smaller than a preset value.
The detection model comprises a candidate region prediction structure, a region judgment structure and a first region prediction structure. Wherein the input of the region decision structure is connected to the output of the candidate region prediction structure and the output of the region decision structure is connected to the input of the first region prediction structure.
Taking the detection model based on fast R-CNN as an example, the detection model described in this embodiment is to implement the detection of the small-size target on the basis of fast R-CNN, and essentially: after stage 5 of the Faster R-CNN backbone ResNet network, another head similar to the RPN (region suggestion network) is connected for predicting whether there is a small-size target in a candidate region with each pixel as the center in the feature map output by stage 5.
The candidate region prediction structure is used for predicting candidate regions of all targets in the image to be detected; the area judgment structure is used for judging whether the size of the candidate area of each target is smaller than a preset value so as to determine the target candidate area; the first region prediction structure is used to determine location information of the target candidate region.
Specifically, the step S22 includes the following steps:
s221, predicting candidate areas corresponding to all targets in the image to be detected by using the candidate area prediction structure in the detection model.
Alternatively, taking the image to be detected as the image of the power transmission line as an example, the electronic device inputs the image of the power transmission line into the detection model, and the candidate region prediction structure in the detection model detects candidate regions of each target in the image of the power transmission line, for example, a candidate region corresponding to an insulating string, a candidate region corresponding to an insulator spontaneous explosion, a candidate region corresponding to a bird nest, and the like.
S222, judging whether the size of the candidate area is smaller than a preset value by using the area judgment structure in the detection model.
After obtaining the candidate regions corresponding to the targets in the image to be detected, the electronic device compares the size of the candidate region corresponding to each target with a preset value. This is because, when the size of the target is large, the size of the corresponding candidate region is also large; when the size of the target is smaller, the size of the corresponding candidate region is smaller. Therefore, the size of the target size can be reflected by using the size of the candidate region, that is, when the size of the candidate region is smaller than the preset value, it indicates that the target corresponding to the candidate region is a small-sized target, and needs to be secondarily detected.
When the size of the candidate region is smaller than the preset value, S223 is performed; otherwise, S224 is performed.
The electronic device may compare the sizes of the candidate regions by using the number of pixels with at least one side length of the candidate regions, or by using the size of the candidate regions. When the preset value is the number of the pixel points, the electronic equipment compares the number of the pixel points of the side length of each side of the candidate area with the preset value; for example, the preset value is 50 pixels, then when at least one side of the candidate region is less than 50 pixels, S223 is executed; otherwise, S224 is performed. That is, when at least one side of the candidate region is less than 50 pixels, it indicates that the target corresponding to the candidate region is a small-sized target; otherwise, the target corresponding to the candidate area is a large-size target.
S223, predicting the first position information of the candidate region by using the first region prediction structure in the detection model to obtain the position information of the target candidate region.
When the electronic device determines in S222 that the size of the candidate region is smaller than the preset value, the candidate region is the target candidate region, and then the electronic device predicts the first location information of the candidate region by using the first region prediction structure, so as to obtain the first location information of the target candidate region. The first position information may be center position information of the target candidate region, or may be an upper left corner coordinate and a lower right corner coordinate of the target candidate region, or the like.
In this embodiment, the first position information is taken as the center position information of the target candidate region as an example. Because the other position information of the target candidate region is obtained by calculating the center position information of the target candidate region, the center position information of the target candidate region is selected as the first position information, so that the data processing amount can be reduced, and the target detection efficiency can be improved.
S224, the type and the second position information of the object corresponding to the candidate region are predicted by using the second region prediction structure in the detection model.
When the electronic device determines that the size of the candidate frame is greater than or equal to the preset value, it indicates that the target corresponding to the candidate area is a large-size target, and then the electronic device directly predicts the category and the second position information of the target corresponding to the candidate area by using the second area prediction structure in the detection model.
Wherein the input of the second region prediction structure is connected to the output of the region judgment structure. The second position information may be coordinates of a center point of the candidate region, or coordinates of an upper left corner and a lower right corner of the candidate region.
And S23, extracting an image with a preset size from the image to be detected based on the position information of the target candidate area to obtain a sub-image to be detected.
Please refer to S13 in fig. 1, which is not described herein again.
And S24, inputting the sub-image to be detected into the detection model to obtain the target category corresponding to the target candidate area.
Please refer to S14 in fig. 1, which is not described herein again.
In the target detection method provided by this embodiment, the size of the candidate region is determined by using the region determination structure, and when the size is smaller than the preset value, the first position information of the candidate region is predicted only by using the first region preset structure without performing category prediction, so that the target detection efficiency can be improved on the premise of ensuring the detection accuracy.
In this embodiment, a target detection method is provided, which can be used in electronic devices, such as a mobile phone, a tablet computer, a computer, etc., fig. 3 is a flowchart of the target detection method according to the embodiment of the present invention, and as shown in fig. 3, the flowchart includes the following steps:
and S31, acquiring an image to be detected.
Please refer to S21 in fig. 2 for details, which are not described herein.
And S32, inputting the image to be detected into the detection model to obtain the position information of the target candidate area.
And the target candidate region is a candidate region of which the sizes of all the candidate regions output by the detection model are smaller than a preset value.
Specifically, the step S32 includes the following steps:
s321, zooming the image to be detected to the image to be detected with the preset resolution.
After the electronic equipment acquires the image to be detected, the image to be detected is zoomed to the image to be detected with the preset resolution. The preset resolution may be specifically set according to actual conditions, for example, the image to be detected may be scaled to 512 × 512 resolution.
Taking the image to be detected as the image of the power transmission line as an example, please refer to fig. 4a, and fig. 4a shows a specific schematic diagram of the image to be detected. The targets in the transmission line image are a grading ring, an insulating string, a bird nest and a vibration damper.
S322, inputting the image to be detected with the preset resolution into the detection model to obtain the position information of the target candidate region.
The electronic device inputs the image to be detected shown in fig. 4a into the detection model to obtain the position information of the target candidate region as shown in fig. 4 b. Fig. 4a is a schematic diagram of a typical power transmission line image, wherein the diagram includes 7 targets of 4 types of grading rings, insulator strings, bird nests and damper. After the to-be-detected image shown in fig. 4a is scaled to 512 × 512 pixels and input into the detection model of this embodiment by the electronic device, the detection model detects and outputs the category numbers and the coordinates of the upper left corner and the lower right corner of the two insulator string targets shown by the bold frame in fig. 4 b. Since the side lengths of the candidate regions corresponding to the strap, the bird nest, and the damper in fig. 4b are all less than 50 pixels in the zoomed image, the detection model will identify and output the center coordinates of the 3 candidate regions containing the small-sized object as indicated by the arrows in fig. 4b, as shown by the dotted lines in fig. 4 b.
The candidate regions indicated by arrows in fig. 4b are the target candidate regions.
For the rest, please refer to S22 in the embodiment shown in fig. 2, which is not described herein again.
And S33, extracting an image with a preset size from the image to be detected based on the position information of the target candidate area to obtain a sub-image to be detected.
After obtaining the position information of the target candidate region in S32, the electronic device extracts an image of a preset size from the image to be detected. Specifically, as described above, the target candidate region is a candidate region corresponding to a small-sized target. Referring to fig. 4c, the small-sized targets in the image to be detected are the equalizer ring, the bird nest, and the anti-vibration hammer, and then the target candidate regions are candidate regions corresponding to the equalizer ring, the bird nest, and the anti-vibration hammer.
Then, the electronic device corresponds the position information to the original image to be detected, extracts an image with a size of 128 × pixels, and obtains a sub-image to be detected as shown in fig. 4 c.
And S34, inputting the sub-image to be detected into the detection model to obtain the target category corresponding to the target candidate area.
Specifically, the step S34 includes the following steps:
and S341, zooming the sub-image to be detected to the sub-image to be detected with the preset resolution.
After obtaining the to-be-detected subimage in S33, the electronic device scales the to-be-detected subimage to the preset resolution in S321, so as to obtain the to-be-detected subimage with the preset resolution.
And S342, inputting the image to be detected with the preset resolution into the detection model to obtain the target category corresponding to the target candidate area.
The electronic equipment inputs the subimage to be detected with the preset resolution into the detection model, and predicts the candidate area corresponding to the target in the subimage to be detected so as to obtain the category of the target corresponding to the subsequent area of the target.
As described above, the size of the to-be-detected sub-image is 128 × 128 pixels, the to-be-detected sub-image is scaled to 512 × 512 pixels, the scaled position of the target candidate region in the to-be-detected image is obtained by inputting the scaled position of the target candidate region in the to-be-detected image, the relative position of the target corresponding to the target candidate region in the to-be-detected image is obtained, the to-be-detected sub-image is extracted from the to-be-detected image according to each position information, and the step S32-S34 is repeated with the to-be-detected sub-image as the power transmission line image (i.e., each to-be-detected sub-image is scaled to 512 × 512 pixels and is.
Specifically, the electronic device cuts the image of the corresponding region in the image to be detected of the three target candidate regions shown in fig. 4b in the image to be detected shown in fig. 4a into sub-images to be detected, scales the sub-images to be detected to 512 × 512 pixels, and sequentially inputs the scaled sub-images into the detection model of this embodiment, and the detection model identifies and outputs the category numbers and the coordinates of the top left corner and the bottom right corner of the two equalizer rings in the first sub-image and the two damper hammers in the third sub-image, which are shown in fig. 4c by bold frames. The target candidate area corresponding to the bird nest in the second sub-picture is smaller than 50 pixels in the zoomed sub-picture to be detected, and the detection model identifies and outputs the center coordinates of the block shown by the dotted line in the sub-picture to be detected.
The corresponding area of the block shown in fig. 4c in the image to be detected is cut into the sub-image to be detected, scaled to 512 × 512 pixels and input into the detection model described in this embodiment, so that the bird nest in the sub-image to be detected can be identified.
Please refer to S14 in fig. 1 for further details, which are not described herein again.
In the target detection method provided by this embodiment, before the image to be detected is detected, the image to be detected is scaled to the image to be detected with the preset resolution, so as to improve the detection accuracy.
In the example shown in fig. 4a, the total number of pixels of the processed image in the method of the present embodiment is only 4 × 512 pixels (1048576 pixels in total), however, the recognition effect on the small-size target is approximately equal to the effect of scaling the original image into 8192 × 8192 pixels (67108864 pixels in total) and performing the recognition. Therefore, the method of the embodiment ensures the identification precision, greatly reduces the calculation amount of the power transmission line parts and the defect detection, and improves the detection efficiency.
In accordance with an embodiment of the present invention, there is provided an embodiment of a training method for a test model, it is noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer-executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
In this embodiment, a training method of a detection model is provided, which can be used in electronic devices, such as a mobile phone, a tablet computer, a computer, and the like, fig. 5 is a flowchart of a target detection method according to an embodiment of the present invention, and as shown in fig. 5, the flowchart includes the following steps:
and S41, acquiring the sample image with the annotation information.
The labeling information is the category corresponding to each target in the sample image and the position information of a target area, and the size of the target area is smaller than a preset value.
The labeling of the sample image can be manually labeled or automatically labeled by using a labeling tool. That is, the sample image is labeled with the category corresponding to each object and the position information of the object region corresponding to the object candidate region in the embodiment shown in fig. 1 to 3, which all correspond to the small-sized object.
S42, the sample image is input into the initial detection model to obtain the location information of the prediction region.
The electronic equipment inputs the sample image with the labeling information into the initial detection model to obtain the position information of the prediction area, wherein the prediction area is a prediction area corresponding to the small-size target.
And S43, updating parameters in the initial detection model based on the position information of the target area marked in the sample image and the position information of the prediction area to obtain the detection model.
The electronic device calculates a loss function using the position information of the prediction region obtained in S42 and the position information of the target region marked in S41, and updates the parameters in the initial detection model to obtain a detection model.
According to the training method for the detection model, the detection model is used for predicting the small-size target, the accuracy of small-size target detection can be guaranteed, and accurate guarantee is provided for the follow-up detection of the small-size target by using the detection model.
In this embodiment, a target detection apparatus is further provided, and the apparatus is used to implement the foregoing embodiments and preferred embodiments, and the description of the apparatus is omitted for brevity. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
The present embodiment provides an object detection apparatus, as shown in fig. 6, including:
a first obtaining module 51, configured to obtain an image to be detected;
the detection module 52 is configured to input the image to be detected into a detection model to obtain position information of the target candidate region; the target candidate region is a candidate region of which the sizes of all candidate regions output by the detection model are smaller than a preset value;
an extracting module 53, configured to extract an image with a preset size from the image to be detected based on the position information of the target candidate region, so as to obtain a sub-image to be detected;
the first input module 54 is configured to input the sub-image to be detected into the detection model, so as to obtain a category of the target corresponding to the target candidate region.
In the target detection device provided by this embodiment, after the position information of the target candidate region is obtained by using the detection model, an image with a preset size is extracted from the image to be detected to obtain a sub-image to be detected, and the detection model is used to detect the sub-image to be detected again; that is, only the candidate region having the size smaller than the preset size is extracted from the image to be detected and the detection is performed again, so that the data processing amount can be reduced and the target detection efficiency can be improved.
The embodiment further provides a training apparatus for detecting a model, as shown in fig. 7, including:
the second obtaining module 61 is configured to obtain a sample image with labeling information; the labeling information is the category corresponding to each target in the sample image and the position information of a target area, and the size of the target area is smaller than a preset value;
a second input module 62, configured to input the sample image into the initial detection model to obtain location information of the prediction region;
an updating module 63, configured to update parameters in the initial detection model based on the position information of the target region marked in the sample image and the position information of the prediction region, so as to obtain the detection model.
The training device for the detection model provided by the embodiment predicts the small-size target by using the detection model, can ensure the accuracy of the detection of the small-size target, and provides accurate guarantee for the subsequent detection of the small-size target by using the detection model.
The object detection device, or training device for detecting a model, in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC circuit, a processor and memory executing one or more software or fixed programs, and/or other devices that can provide the above-described functionality.
Further functional descriptions of the modules are the same as those of the corresponding embodiments, and are not repeated herein.
An embodiment of the present invention further provides an electronic device, which has the target detection apparatus shown in fig. 6 or the training apparatus of the detection model shown in fig. 7.
Referring to fig. 8, fig. 8 is a schematic structural diagram of an electronic device according to an alternative embodiment of the present invention, and as shown in fig. 8, the electronic device may include: at least one processor 71, such as a CPU (Central Processing Unit), at least one communication interface 73, memory 74, at least one communication bus 72. Wherein a communication bus 72 is used to enable the connection communication between these components. The communication interface 73 may include a Display (Display) and a Keyboard (Keyboard), and the optional communication interface 73 may also include a standard wired interface and a standard wireless interface. The Memory 74 may be a high-speed RAM Memory (volatile Random Access Memory) or a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The memory 74 may alternatively be at least one memory device located remotely from the processor 71. Wherein the processor 71 may be in connection with the apparatus described in fig. 6 or fig. 7, an application program is stored in the memory 74, and the processor 71 calls the program code stored in the memory 74 for performing any of the above-mentioned method steps.
The communication bus 72 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The communication bus 72 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 8, but this is not intended to represent only one bus or type of bus.
The memory 74 may include a volatile memory (RAM), such as a random-access memory (RAM); the memory may also include a non-volatile memory (english: non-volatile memory), such as a flash memory (english: flash memory), a hard disk (english: hard disk drive, abbreviation: HDD), or a solid-state drive (english: SSD); the memory 74 may also comprise a combination of memories of the kind described above.
The processor 71 may be a Central Processing Unit (CPU), a Network Processor (NP), or a combination of CPU and NP.
The processor 71 may further include a hardware chip, which may be an application-specific integrated circuit (ASIC), a programmable logic device (CP L D), or a combination thereof, and the P L D may be a complex programmable logic device (CP L D), a field-programmable gate array (FPGA), a general-purpose array logic (GA L), or any combination thereof.
Optionally, the memory 74 is also used for storing program instructions. The processor 71 may call program instructions to implement a target detection method as shown in the embodiments of fig. 1 to 3 of the present application, or a training method of a detection model as shown in the embodiment of fig. 5.
The embodiment of the invention also provides a non-transitory computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions can execute the target detection method or the training method of the detection model in any method embodiment. The storage medium may be a magnetic Disk, an optical Disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a flash Memory (FlashMemory), a Hard Disk (Hard Disk Drive, abbreviated as HDD) or a Solid State Drive (SSD), etc.; the storage medium may also comprise a combination of memories of the kind described above.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (10)
1. A method of object detection, comprising:
acquiring an image to be detected;
inputting the image to be detected into a detection model to obtain the position information of a target candidate region; the target candidate region is a candidate region of which the sizes of all candidate regions output by the detection model are smaller than a preset value;
extracting an image with a preset size from the image to be detected based on the position information of the target candidate area to obtain a sub-image to be detected;
and inputting the sub-image to be detected into the detection model to obtain the category of the target corresponding to the target candidate area.
2. The method according to claim 1, wherein the inputting the image to be detected into a detection model to obtain the position information of the target candidate region comprises:
predicting candidate areas corresponding to all targets in the image to be detected by using a candidate area prediction structure in the detection model;
judging whether the size of the candidate region is smaller than the preset value by using a region judgment structure in the detection model;
and when the size of the candidate region is smaller than the preset value, predicting first position information of the candidate region by using a first region prediction structure in the detection model to obtain the position information of the target candidate region.
3. The method according to claim 2, wherein the first position information is center position information of the target candidate region.
4. The method according to claim 2, wherein the inputting the image to be detected into a detection model to obtain the position information of the target candidate region further comprises:
and when the size of the candidate region is larger than or equal to the preset value, predicting the category and the second position information of the target corresponding to the candidate region by using a second region prediction structure in the detection model.
5. The method according to any one of claims 1-4, wherein the inputting the image to be detected into a detection model to obtain the position information of the target candidate region further comprises:
zooming the image to be detected to an image to be detected with a preset resolution;
and inputting the image to be detected with the preset resolution into the detection model to obtain the position information of the target candidate region.
6. The method according to claim 5, wherein the inputting the sub-image to be detected into the detection model to obtain the category of the target corresponding to the target candidate region comprises:
zooming the sub-image to be detected to the sub-image to be detected with the preset resolution;
and inputting the sub-image to be detected with the preset resolution into the detection model to obtain the category of the target corresponding to the target candidate area.
7. The method according to claim 1, characterized in that the image to be detected is a transmission line image.
8. A training method for a detection model is characterized by comprising the following steps:
acquiring a sample image with marking information; the labeling information is the category corresponding to each target in the sample image and the position information of a target area, and the size of the target area is smaller than a preset value;
inputting the sample image into an initial detection model to obtain the position information of a prediction region;
and updating parameters in the initial detection model based on the position information of the target area marked in the sample image and the position information of the prediction area to obtain the detection model.
9. An electronic device, comprising:
a memory and a processor, the memory and the processor being communicatively connected to each other, the memory having stored therein computer instructions, the processor executing the computer instructions to perform the object detection method according to any one of claims 1 to 7 or the training method of the detection model according to claim 8.
10. A computer-readable storage medium storing computer instructions for causing a computer to perform the object detection method of any one of claims 1 to 7 or the training method of the detection model of claim 8.
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