文献类型：期刊文献

英文题名：Anomaly Detection Algorithm for Photovoltaic Cells Based on Lightweight Multi-Channel Spatial Attention Mechanism

作者：Chen, Aidong[1,2,3];Li, Xiang[1,2];Jing, Hongyuan[2,3];Hong, Chen[2,3];Li, Minghai[2,3]

第一作者：Chen, Aidong;陈艾东

通讯作者：Li, MH[1];Li, MH[2]

机构：[1]Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China;[2]Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China;[3]Res Ctr Multiintelligent Syst, Beijing 100101, Peoples R China

第一机构：Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China;[2]corresponding author), Res Ctr Multiintelligent Syst, Beijing 100101, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;

年份：2023

卷号：16

期号：4

外文期刊名：ENERGIES

收录：;EI(收录号：20231013668456);Scopus(收录号：2-s2.0-85149171781);WOS:【SCI-EXPANDED(收录号:WOS:000939167900001)】；

基金：This research was supported by the National key research and development plan " Multidimensional visual information edge intelligent processor chip" (2022YFB2804402).

语种：英文

外文关键词：photovoltaic cell; electroluminescence; defect detection; image recognition

摘要：With the proposed goal of "Carbon Neutrality", photovoltaic energy is gradually gaining the leading role in energy transformation. At present, crystalline silicon cells are still the mainstream technology in the photovoltaic industry, but due to the similarity of defect characteristics and the small scale of the defects, automatic defect detection of photovoltaic cells (PV) by electroluminescence (EL) imaging is a challenging task. In order to better meet the growing demand for high-quality photovoltaic cell products in intelligent manufacturing and use, and ensure the safe and efficient operation of photovoltaic power stations, this paper proposes an improved abnormal detection method based on Faster R-CNN for the surface defect EL imaging of photovoltaic cells, which integrates a lightweight channel and spatial convolution attention module. It can analyze the crack defects in complex scenes more efficiently. The clustering algorithm was used to obtain a more targeted anchor frame for photovoltaic cells, which made the model converge faster and enhanced the detection ability. The normalized distance between the prediction box and the target box is minimized by considering the DIoU loss function for the overlapping area of the boundary box and the distance between the center points. The experiment shows that the average accuracy of surface defect detection for EL images of photovoltaic cells is improved by 14.87% compared with the original algorithm, which significantly improves the accuracy of defect detection. The model can better detect small target defects, meet the requirements of surface defect detection of photovoltaic cells, and proves that it has good application prospects in the field of photovoltaic cell defect detection.