The invention discloses a high-precision real-time photovoltaic panel multi-
source image end-to-end defect detection method and
system, and relates to the technical field of photovoltaic detection and
computer vision. The objective of the invention is to solve the problems of poor single-mode adaptability, insufficient multi-source fusion, poor real-time performance and no closed-
loop optimization in the prior art. According to the method,
infrared and visible light features are extracted through a double-
branch network containing a GRB module and an SGB module, after fusion is conducted through an FB module, defect detection is completed through a PANet and a YOLO detection head with an attention module, and an optimized
closed loop is formed by combining edge deployment testing and feedback adjustment. The
system comprises modules such as a double-
branch encoder and the like, and the performance is improved by adopting a lightweight
network architecture and a reasoning optimization technology. On an
edge computing platform, the time consumption of single-frame reasoning is less than or equal to 35ms, the defect mAP reaches more than 93%, the
false alarm rate in a strong light environment is less than or equal to 4%, the method is suitable for automatic inspection of a large-scale
photovoltaic power station, and the operation and maintenance cost can be reduced.