RAFBSD: An Efficient Detector for Accurate Identification of Defects in Photovoltaic Cells

Abstract

Currently, defect detection for photovoltaic (PV) electroluminescence (EL) images faces three challenges: limited training data and complex backgrounds result in low accuracy in detecting defects; the diverse shapes of specific defects often lead to frequent false alarms; and existing models still require improvement in accurately recognizing these 12 specific defects. An innovative detector, called the Reconfigurable Adaptive Focus Background Suppression Detector (RAFBSD), is proposed to tackle these challenges. This approach utilizes the advanced Deformable Attention Transformer (DAT) technology to effectively focus on regions with defects. By doing so, it captures more informative features while significantly reducing noise interference. Additionally, the Omni-dimensional Dynamic Convolution (ODConv) and the Receptive-Field Attention convolutional (RFAConv) modules enhance accuracy by effectively allocating attention weights to optimize model performance. Compared to YOLOv8, RAFBSD has demonstrated substantial enhancements in both mAP50 (by 4.7%) and mAP50-95 (by 5.3%). Furthermore, RAFBSD demonstrates an outstanding capability to precisely identify flaws in 12 different classifications of PVEL images, exceeding the recognition capacity of YOLOv8 models by three additional types. These findings unequivocally establish RAFBSD as an efficient and groundbreaking object detection model.