Photovoltaic hot spot detection method incorporating knowledge distillation and attention mechanisms

A detection algorithm combining knowledge distillation and attention mechanism is proposed to solve the problem that multi-scale target of the hot spot fault of photovoltaic panel in a complex environ⁃ ment leads to difficult detection. To efficiently extract and retain fault feature information，a module that integrates higher-order spatial interaction and channel attention was designed to improve the expression ability of fault feature information. To further enhance the ability of expressing target information in a com⁃ plex background，an attention module combining channel and location information was constructed to im⁃ prove the recognition accuracy of fault location information. The parameters of teacher network were trans⁃ ferred to student network by knowledge distillation，and the detection accuracy of student network was im⁃ proved without adding any complexity. A focal-CIoU loss function was introduced to accelerate network convergence and improve detection performance. In verifying the effectiveness of the proposed algorithm against eight classical algorithms，the experimental results show that the proposed algorithm has the high⁃ est detection accuracy（84. 8%），and the detection speed can reach 142 FPS for images with a resolution of 640×512. © 2023 Chinese Academy of Sciences. All rights reserved.