Surface anomaly detection on photovoltaic (PV) panels is crucial for their operation and maintenance,es-pecially in island environments where challenges such as small anomaly sizes and minimal color differences are prevalent. Due to the poor accuracy and low efficiency of existing detection methods,the paper proposes a surface anomaly detection method for island-based PV panels using edge neural networks. First,by use of convolutional neu-ral networks (CNNs) and attention mechanisms,an anomaly detection model,characterized by multi-scale feature fusion,is constructed to explore the features of fine-grained anomalies,thereby enhancing the surface anomaly de-tection accuracy. Additionally,a dual dynamic model compression technique is employed to reduce redundant chan-nels and feature blocks,significantly lowering the model's computational complexity and enabling rapid and accu-rate anomaly detection. The proposed method demonstrates strong performance in surface anomaly detection on PV panels in Zhoushan,highlighting its effectiveness and superiority.
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