Photovoltaic Panel Defect Detection Based on Ghost Convolution with BottleneckCSP and Tiny Target Prediction Head Incorporating YOLOv5

• URL: http://arxiv.org/abs/2303.00886v1
• Date: Thu, 2 Mar 2023 01:06:35 GMT
• Title: Photovoltaic Panel Defect Detection Based on Ghost Convolution with BottleneckCSP and Tiny Target Prediction Head Incorporating YOLOv5
• Authors: Longlong Li, Zhifeng Wang, Tingting Zhang
• Abstract summary: This paper proposes an approach named Ghost convolution with BottleneckCSP and a tiny target prediction head for PV panel defect detection. The BottleneckCSP module is introduced to add a prediction head for tiny target detection to alleviate tiny defect misses. The proposed PV panel surface-defect detection network improves the mAP performance by at least 27.8%.
• Score: 5.632384612137748
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Photovoltaic (PV) panel surface-defect detection technology is crucial for the PV industry to perform smart maintenance. Using computer vision technology to detect PV panel surface defects can ensure better accuracy while reducing the workload of traditional worker field inspections. However, multiple tiny defects on the PV panel surface and the high similarity between different defects make it challenging to {accurately identify and detect such defects}. This paper proposes an approach named Ghost convolution with BottleneckCSP and a tiny target prediction head incorporating YOLOv5 (GBH-YOLOv5) for PV panel defect detection. To ensure better accuracy on multiscale targets, the BottleneckCSP module is introduced to add a prediction head for tiny target detection to alleviate tiny defect misses, using Ghost convolution to improve the model inference speed and reduce the number of parameters. First, the original image is compressed and cropped to enlarge the defect size physically. Then, the processed images are input into GBH-YOLOv5, and the depth features are extracted through network processing based on Ghost convolution, the application of the BottleneckCSP module, and the prediction head of tiny targets. Finally, the extracted features are classified by a Feature Pyramid Network (FPN) and a Path Aggregation Network (PAN) structure. Meanwhile, we compare our method with state-of-the-art methods to verify the effectiveness of the proposed method. The proposed PV panel surface-defect detection network improves the mAP performance by at least 27.8%.

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