PHOTOVOLTAIC DEFECT DETECTION BASED ON MULTI-SCALE CODING COMPLEMENTARY ATTENTION NETWORK

Chen Haiyong; Yuan Le; Wang Shijie; Zhao Shenshen

doi:10.19912/j.0254-0096.tynxb.2022-0966

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (10) : 191-197. DOI: 10.19912/j.0254-0096.tynxb.2022-0966

PHOTOVOLTAIC DEFECT DETECTION BASED ON MULTI-SCALE CODING COMPLEMENTARY ATTENTION NETWORK

Chen Haiyong, Yuan Le, Wang Shijie, Zhao Shenshen

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Abstract

The defect detection for photovoltaic module electroluminescence images is a challenging task, due to two difficulties, tiny and weak. To address this problem, the Multi-Scale Encoding Complementary Attention Network （MCECAN） is designed. The backbone and prediction head of MCECAN follow the YOLO series design, but the network neck applies the Multi-Scale Coding Complementary Attention Module （MCECAM）. The front-end of the module uses a multi-scale encoder to aggregate multi-scale information and enhance global information. The back-end complementary coordinate attention establishes the dependency between feature map channels, highlights defect features, suppresses background interference, and improves the ability of network to detect tiny and weak targets. On a dataset containing 5537 EL defect images, the MCECAN shows the best detection performance.

Key words

photovoltaic modules / defect detection / convolutional neural network / multi-scale encoder / complementary coordinate attention

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Chen Haiyong, Yuan Le, Wang Shijie, Zhao Shenshen. PHOTOVOLTAIC DEFECT DETECTION BASED ON MULTI-SCALE CODING COMPLEMENTARY ATTENTION NETWORK[J]. Acta Energiae Solaris Sinica. 2023, 44(10): 191-197 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0966

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