Research on Surface Defect Detection of Solar Pv Panels Based on Pre-Training Network and Feature Fusion

Li, Huohua; Fu, Xiaogang; Huang, Tairu

doi:10.1088/1755-1315/651/2/022071

Cited by 5 publications

(7 citation statements)

References 3 publications

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“…On the whole, Qian 14 can only identify whether there is a fault, so only the average accuracy is compared. The average accuracy of all faults of the improved method is improved from 86.7% to 89.39%; although the average recognition accuracy of all faults in Li 16 is slightly worse, in common hidden crack faults, the accuracy is improved from 89.29% to 91.93%, and in black core faults, the recognition accuracy of the article is also higher than that of this method. At the same time, compared with Li 16 , the article increases the recognition of grid breaking faults, and the recognition effect is good.…”

Section: Experimental Analysismentioning

confidence: 83%

“…the accuracy of fragment fault and black core fault has been significantly improved. At the same time, the paper compares two fault identification methods in Qian 14 and Li 16 . On the whole, Qian 14 can only identify whether there is a fault, so only the average accuracy is compared.…”

Section: Experimental Analysismentioning

confidence: 99%

“…Ma 15 proposes an algorithm based on Improved RBF neural network for fault diagnosis of photovoltaic modules, and the fault diagnosis rate is greatly improved compared with that before improvement. For the problem of solar panel fault diagnosis, Li 16 proposed a deep classification network model based on improved VGG19, which improves the accuracy of fault identification. However, VGG19 network consumes more computing resources and takes more time in operation.…”

Section: Introductionmentioning

confidence: 99%

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Solar panel fault diagnosis method based on improved YOLOv3

Jiang¹,

Wang²,

Zhang³

et al. 2022

Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

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The quality of solar panels determines the efficiency of photovoltaic power generation. With the rapid development of the photovoltaic industry, the quality of solar panels has gradually become the focus of the industry. The failure of solar panels limits the photoelectric conversion efficiency and service life of the panels, and poses a huge challenge to the overall safety of the photovoltaic system. Therefore, this article proposes a solar panel fault diagnosis method based on the YOLOv3 algorithm. The algorithm optimizes the learning rate configuration, the determination of the optimal anchor frame, and the avoidance of identifying multiple anchor frame parts on the basis of the YOLOv3 algorithm. And it can detect many different types of target failure points at the same time. The experimental results verify the effectiveness of the algorithm.

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Section: Experimental Analysismentioning

confidence: 83%

Section: Experimental Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Solar panel fault diagnosis method based on improved YOLOv3

Jiang¹,

Wang²,

Zhang³

et al. 2022

Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

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“…However, their defect features need to be manually extracted and the resulting low-dimensional artificial features are difficult to generalize to complex strip surface defects. Therefore, the application of these methods needs to be combined with specific scenarios [15,16].…”

Section: Related Workmentioning

confidence: 99%

Improving the Performance of the Single Shot Multibox Detector for Steel Surface Defects with Context Fusion and Feature Refinement

Zhang

et al. 2023

Electronics

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Strip surface defects have large intraclass and small interclass differences, resulting in the available detection techniques having either a low accuracy or very poor real-time performance. In order to improve the ability for capturing steel surface defects, the context fusion structure introduces the local information of the shallow layer and the semantic information of the deep layer into multiscale feature maps. In addition, for filtering the semantic conflicts and redundancies arising from context fusion, a feature refinement module is introduced in our method, which further improves the detection accuracy. Our experimental results show that this significantly improved the performance. In particular, our method achieved 79.5% mAP and 71 FPS on the public NEU-DET dataset. This means that our method had a higher detection accuracy compared to other techniques.

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“…Presently, migration learning methods for blemish detection encompass two primary categories: pre-trained model methods and domain adaptive methods. Pre-trained model approaches [31,32] often overlook the intrinsic similarity between source and target domain data, leading the target network to acquire task-irrelevant features, thereby impeding the effectiveness of migration. Conversely, domain adaptive methods facilitate the transfer of pertinent features learned by the source domain model to the target task by identifying shared features or similarities between the domains [33].…”

Section: Introductionmentioning

confidence: 99%

Fabric Defect Detection Method Using SA-Pix2pix Network and Transfer Learning

Hu,

Gong,

et al. 2023

Applied Sciences

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This paper proposes a fabric defect detection algorithm based on the SA-Pix2pix network and transfer learning to address the issue of insufficient accuracy in detecting complex pattern fabric defects in scenarios with limited sample data. Its primary contribution lies in treating defects as disruptions to the fabric’s texture. It leverages a generative adversarial network to reconstruct defective images, restoring them to images of normal fabric texture. Subsequently, the reconstituted images are subjected to dissimilarity calculations against defective images, leading to image segmentation for the purpose of defect detection. This approach addresses the issues of poor defect image reconstruction accuracy due to the limited ability of remote dependency modeling within the generator’s convolutional neural network. It also tackles deficiencies in the generative adversarial network’s loss function in handling image details. To enhance the structure and loss function of the generative adversarial network, it introduces self-attention mechanisms, L1 loss, and an improved structural loss, thus mitigating the problems of low defect image reconstruction accuracy and insufficient image detail handling by the network. To counteract the issue of declining model training accuracy in the face of sparse complex fabric defect samples, a channel-wise domain transfer learning approach is introduced. This approach constrains the training of the target network through feature distribution, thereby overcoming the problem of target network overfitting caused by limited sample data. The study employs three methods to experimentally compare and investigate five distinct complex pattern fabric defects. The results demonstrate that, when compared to two other defect detection methods, the approach advocated in this paper exhibits superior detection accuracy in scenarios with limited sample data.

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Research on Surface Defect Detection of Solar Pv Panels Based on Pre-Training Network and Feature Fusion

Cited by 5 publications

References 3 publications

Solar panel fault diagnosis method based on improved YOLOv3

Solar panel fault diagnosis method based on improved YOLOv3

Improving the Performance of the Single Shot Multibox Detector for Steel Surface Defects with Context Fusion and Feature Refinement

Fabric Defect Detection Method Using SA-Pix2pix Network and Transfer Learning

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