Journal of Shandong University(Engineering Science) ›› 2025, Vol. 55 ›› Issue (4): 9-17.doi: 10.6040/j.issn.1672-3961.0.2024.170

• Special Issue for Deep Learning with Vision • Previous Articles    

Photovoltaic defect detection based on fine-grained feature enhancement and scale matching

SUO Daxiang, LI Bo*   

  1. College of Management and Economics, Tianjin University, Tianjin 300072, China
  • Published:2025-08-31

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Abstract: A high-precision defect detection method for photovoltaic panels was proposed to address the issue of missed and false detections associated with small defect targets in unmanned aerial vehicle(UAV)inspections of photovoltaic power stations. Considering the distribution characteristics of target sizes identified during UAV inspections, a strategy that combines fine-grained feature enhancement with scale matching was introduced to enhance the accuracy of small target defect detection in photovoltaics. Distinguished from traditional small target enhancement strategies including data augmentation, multi-scale learning, and feature enhancement, a detail-preserving semantic enhancement module was incorporated to retain fine-grained details and to mine related coarse-grained semantic details. A multi-scale detection strategy featuring anchor-prediction head matching was introduced to ensure the compatibility of anchor sizes with feature maps. The method achieved an average mean precision of 59.4% on the PVEL-AD dataset and 97.6% on the CARPK dataset, significantly improving the performance of photovoltaic defect target detection compared to mainstream object detection models.

Key words: small target detection, real-time object detection, defect detection, feature enhancement, scale matching

CLC Number: 

  • TP391
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