CN113469955A - Photovoltaic module fault area image detection method and system - Google Patents
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Abstract
The invention provides a method and a system for detecting images of a fault area of a photovoltaic module, wherein the method comprises the following steps: acquiring an image to be input, and performing image characteristic enhancement and data enhancement on the image to be input; and performing target detection on the image after the characteristic enhancement and the data enhancement based on a preset neural network, and determining a fault area of the photovoltaic assembly in the image. The embodiment of the invention provides a method and a system for detecting images of a fault area of a photovoltaic module, and provides a novel neural network for detecting the images of the photovoltaic module, fusing rich context information in a characteristic diagram, and combining a decentralized attention mechanism, simplifying a model structure, improving the detection accuracy of the images and improving the detection speed.
Description
Technical Field
The invention relates to the technical field of target detection, in particular to a method and a system for detecting images of a fault area of a photovoltaic module.
Background
The traditional photovoltaic defect detection method generally adopts a template matching method, but the inclination angle of a photovoltaic module in different photovoltaic power stations may change, and the time complexity for detecting the defects of the photovoltaic panel by using the template matching is higher. In addition, the boundary of the defects of the photovoltaic module is fuzzy, and the accurate defect area is difficult to detect only by using a template matching method. In order to improve the accuracy of the defect detection of the photovoltaic module, a YOLO algorithm in the field of target detection is introduced, and the position of the defect of the photovoltaic module is detected by using a deep learning method.
Therefore, a method and a system for detecting an image of a failure area of a photovoltaic module are needed to solve the problem.
Disclosure of Invention
The invention provides a method and a system for detecting images of a fault area of a photovoltaic module, and provides a novel idea that a neural network detects the images of the photovoltaic module, fuses abundant context information in a characteristic diagram, and improves the detection accuracy of the images.
In a first aspect, an embodiment of the present invention provides a method for detecting an image of a fault area of a photovoltaic module, including:
acquiring an image to be input, and performing image characteristic enhancement and data enhancement on the image to be input;
and performing target detection on the image after the characteristic enhancement and the data enhancement based on a preset neural network, and determining a fault area of the photovoltaic assembly in the image.
Wherein the image feature enhancement and data enhancement comprises:
performing image preprocessing on the image, the image preprocessing comprising:
and homomorphic filtering and median filtering are carried out on the image.
Wherein the image feature enhancement and data enhancement further comprises:
and carrying out image noise addition, brightness adjustment, image rotation, image cutting, image translation and mirror image transformation on the image.
The method for detecting the target of the image after the characteristic enhancement and the data enhancement based on the preset neural network and determining the fault area of the photovoltaic assembly in the image comprises the following steps:
based on a YOLO network structure, ResNeSt is used as a backbone network, and the YOLO network structure is used as a feature fusion module, and image features of different scales are fused.
Wherein, the YOLO-based network structure, with resnestt as the backbone network, includes:
ResNeSt divides the feature map along the channel dimension into several groups and finer grained subgroups, where the feature representation decisions at each group are represented by weighted combinations based on global context information weights.
The method for fusing the image features with different scales by using the YOLO network structure and using the FPT as a feature fusion module comprises the following steps:
and fusing image features of different scales by using the FPT feature pyramid structure so that the feature pyramid has rich context information.
In a second aspect, an embodiment of the present invention provides a photovoltaic module fault area image detection system, including:
the image processing module is used for acquiring an image to be input and performing image characteristic enhancement and data enhancement on the image to be input;
and the target detection module is used for carrying out target detection on the image after the characteristic enhancement and the data enhancement based on a preset neural network and determining the fault area of the photovoltaic assembly in the image.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the steps of the method for detecting an image of a photovoltaic module fault area as provided in the first aspect.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method for detecting an image of a fault area of a photovoltaic module as provided in the first aspect.
The embodiment of the invention provides a method and a system for detecting images of a fault area of a photovoltaic module, and provides a novel neural network for detecting the images of the photovoltaic module, fusing rich context information in a characteristic diagram, and combining a decentralized attention mechanism, simplifying a model structure, improving the detection accuracy of the images and improving the detection speed.
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 those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of an image detection method for a fault area of a photovoltaic module according to an embodiment of the present invention;
FIG. 2 is a flow chart of a data enhancement algorithm provided by an embodiment of the present invention;
FIG. 3 is a schematic block diagram of a neural network provided by an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of an image detection system for a fault area of a photovoltaic module according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram 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.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Fig. 1 is a schematic flow chart of a method for detecting an image of a fault area of a photovoltaic module according to an embodiment of the present invention, as shown in fig. 1, the method includes:
101. acquiring an image to be input, and performing image characteristic enhancement and data enhancement on the image to be input;
102. and performing target detection on the image after the characteristic enhancement and the data enhancement based on a preset neural network, and determining a fault area of the photovoltaic assembly in the image.
It should be noted that fig. 2 is a flowchart of a data enhancement algorithm provided by an embodiment of the present invention, and fig. 3 is a schematic block diagram of a neural network provided by an embodiment of the present invention, as shown in fig. 2 and fig. 3, in step 101, an embodiment of the present invention acquires an image, and performs feature enhancement and data enhancement operations on the image. Then, in step 102, the neural network is used to perform target detection on the input image, and find out the fault area of the photovoltaic module in the image.
The embodiment of the invention provides a method for detecting images of a fault area of a photovoltaic module, which provides a novel neural network for detecting the images of the photovoltaic module, fuses rich context information in a characteristic diagram, combines a decentralized attention mechanism, simplifies a model structure, improves the detection accuracy of the images and improves the detection speed.
On the basis of the above embodiment, in a possible implementation manner, the image feature enhancement and the data enhancement include:
performing image preprocessing on the image, the image preprocessing comprising:
and homomorphic filtering and median filtering are carried out on the image.
It will be appreciated that first, embodiments of the present invention homomorphically filter and median filter the image.
Because the photovoltaic module detects that the picture is formed images in western plateau area large-scale photovoltaic power plant more, can't avoid receiving the influence of environmental variable such as illumination and noise. To reduce the difficulty of target detection, the influence of the environment variables is first attenuated by homomorphic filtering and median filtering.
On the basis of the foregoing embodiment, in a possible implementation manner, the image feature enhancement and the data enhancement further include:
and carrying out image noise addition, brightness adjustment, image rotation, image cutting, image translation and mirror image transformation on the image.
It can be understood that the adopted photovoltaic module string has a single shape and arrangement mode, the number of images contained in the data set is small, the defect types are seriously unbalanced, and overfitting is easily caused by considering that more parameters need to be trained in the YOLO network. Therefore, the data quantity of training is increased in a data enhancement mode, and the generalization capability and robustness of the training samples are improved. Data enhancement includes image denoising, brightness adjustment, image rotation, image cropping, image translation, and mirror transformation of the image.
It should be noted that the embodiment of the present invention scales the image long side to 416, and the remaining gray scale region is filled with (128,128,128), so that the size of the final image is 416 × 3. The picture input reseist structure output feature map size becomes 1/32 for the input image. In order not to lose image information during convolution, the image should be scaled to a multiple of 32 before being input to the YOLO network, the image long edge is scaled to 416, the remaining gray areas are filled with (128,128,128), and finally the input image size is 416 x 3.
On the basis of the foregoing embodiment, in a possible implementation manner, the performing target detection on the image after feature enhancement and data enhancement based on a preset neural network, and determining a fault area of a photovoltaic module in the image includes:
based on a YOLO network structure, ResNeSt is used as a backbone network, and the YOLO network structure is used as a feature fusion module, and image features of different scales are fused.
Specifically, the embodiment of the invention is based on a YOLO network structure, and uses resnext as a Backbone network Backbone. And taking FPT as a feature fusion module of the Neck based on a YOLO network structure.
On the basis of the foregoing embodiment, in a possible implementation manner, the method for implementing a YOLO-based network structure by using resnext as a backbone network includes:
ResNeSt divides the feature map along the channel dimension into several groups and finer grained subgroups, where the feature representation decisions at each group are represented by weighted combinations based on global context information weights.
ResNeSt incorporates a function graph Split Attention mechanism into individual network modules, i.e., the feature graph is divided into several groups and finer-grained subgroups along the channel dimension, wherein the feature representation decisions in each group are represented by weighted combination based on global context information weights, such units are called Split-Attention modules, a ResNet-like network is established by stacking Split-Attention blocks, and the architecture realizes a fixed set of feature graphs by dispersing the Attention of feature graphs into a single network block, is responsible for fixed feature extraction and expression tasks, requires more computations than the existing ResNet variant, and is easily used as the basis for other visual tasks.
On the basis of the foregoing embodiment, in a possible implementation manner, the fusing image features of different scales with a YOLO network structure and with FPT as a feature fusion module includes:
and fusing image features of different scales by using the FPT feature pyramid structure so that the feature pyramid has rich context information.
It can be understood that, in the conventional YOLO detection algorithm, a feature pyramid structure (FPN) is used to fuse image features of different scales, and in order to further improve the effect of image feature fusion, the original feature pyramid structure is replaced with a feature pyramid structure (FPT) based on a Transformer. The FPT makes the characteristic pyramid have the same size but richer context information by using self-level, top-down and bottom-up specially designed transformations on the basis of an FPN structure.
Fig. 4 is a schematic structural diagram of a photovoltaic module fault area image detection system according to an embodiment of the present invention, as shown in fig. 4, including: an image processing module 401 and an object detection module 402, wherein:
the image processing module 401 is configured to acquire an image to be input, and perform image feature enhancement and data enhancement on the image to be input;
the target detection module 402 is configured to perform target detection on the image after the feature enhancement and the data enhancement based on a preset neural network, and determine a fault area of the photovoltaic module in the image.
For how to detect the image of the faulty area of the photovoltaic device by using the image processing module 401 and the target detection module 402, reference may be made to the above method embodiment, and details of the embodiment of the present invention are not repeated herein.
In an embodiment, based on the same concept, an embodiment of the present invention further provides an electronic device, as shown in fig. 5, where fig. 5 illustrates a schematic structural diagram of the electronic device, and the electronic device may include: a processor (processor)501, a communication Interface (Communications Interface)502, a memory (memory)503, and a bus 504, wherein the processor 501, the communication Interface 502, and the memory 503 are configured to communicate with each other via the bus 504. The processor 501 may call logic instructions in the memory 503 to perform the following steps of the road network matching method between heterogeneous high-precision maps, for example, including: acquiring an image to be input, and performing image characteristic enhancement and data enhancement on the image to be input; and performing target detection on the image after the characteristic enhancement and the data enhancement based on a preset neural network, and determining a fault area of the photovoltaic assembly in the image.
In one embodiment, based on the same concept, the present embodiment further provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, the computer program includes program instructions, when the program instructions are executed by a computer, the computer can execute the steps of the road network matching method between the heterogeneous high-precision maps provided by the above-mentioned method embodiments, for example, the steps include: acquiring an image to be input, and performing image characteristic enhancement and data enhancement on the image to be input; and performing target detection on the image after the characteristic enhancement and the data enhancement based on a preset neural network, and determining a fault area of the photovoltaic assembly in the image.
In one embodiment, based on the same concept, the embodiment of the present invention further provides a non-transitory computer-readable storage medium, which stores a computer program, and when the computer program is executed by a computer, the computer program causes the computer to perform the steps of the road network matching method between the heterogeneous high-precision maps provided by the above embodiments, for example, the steps include: acquiring an image to be input, and performing image characteristic enhancement and data enhancement on the image to be input; and performing target detection on the image after the characteristic enhancement and the data enhancement based on a preset neural network, and determining a fault area of the photovoltaic assembly in the image.
It should be noted that, in the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to relevant descriptions of other embodiments for parts that are not described in detail in a certain embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. A photovoltaic module fault area image detection method is characterized by comprising the following steps:
acquiring an image to be input, and performing image characteristic enhancement and data enhancement on the image to be input;
and performing target detection on the image after the characteristic enhancement and the data enhancement based on a preset neural network, and determining a fault area of the photovoltaic assembly in the image.
2. The photovoltaic module fault area image detection method according to claim 1, wherein the image feature enhancement and the data enhancement comprise:
performing image preprocessing on the image, the image preprocessing comprising:
and homomorphic filtering and median filtering are carried out on the image.
3. The photovoltaic module fault area image detection method according to claim 2, wherein the image feature enhancement and data enhancement further comprises:
and carrying out image noise addition, brightness adjustment, image rotation, image cutting, image translation and mirror image transformation on the image.
4. The method for detecting the photovoltaic module fault area image according to claim 1, wherein the step of performing target detection on the image after feature enhancement and data enhancement based on a preset neural network to determine the fault area of the photovoltaic module in the image comprises:
based on a YOLO network structure, ResNeSt is used as a backbone network, and the YOLO network structure is used as a feature fusion module, and image features of different scales are fused.
5. The photovoltaic module fault area image detection method according to claim 4, wherein the YOLO-based network structure with ResNeSt as a backbone network comprises:
ResNeSt divides the feature map along the channel dimension into several groups and finer grained subgroups, where the feature representation decisions at each group are represented by weighted combinations based on global context information weights.
6. The method for detecting the image of the fault area of the photovoltaic module according to claim 4, wherein the fusing image features of different scales by using a YOLO network structure and using FPT as a feature fusion module comprises:
and fusing image features of different scales by using the FPT feature pyramid structure so that the feature pyramid has rich context information.
7. An image detection system for a photovoltaic module fault area, comprising:
the image processing module is used for acquiring an image to be input and performing image characteristic enhancement and data enhancement on the image to be input;
and the target detection module is used for carrying out target detection on the image after the characteristic enhancement and the data enhancement based on a preset neural network and determining the fault area of the photovoltaic assembly in the image.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the image detection method of a photovoltaic module fault area according to any one of claims 1 to 6 when executing the program.
9. A non-transitory computer-readable storage medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, implements the steps of the image detection method for a photovoltaic module fault area according to any one of claims 1 to 6.
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