广西师范大学学报（自然科学版）2026，Vol.44Issue(2)：52-64,13.DOI:10.16088/j.issn.1001-6600.2025060302
基于改进RT-DETR的光伏板缺陷检测
Photovoltaic Panel Defect Detection Based on Improved RT-DETR
摘要
Abstract
In order to address the issues of low accuracy,large model parameters,and the occurrences of missed detections and false detections under complex backgrounds in the existing traditional photovoltaic panel defect detection,this paper proposes an efficient photovoltaic panel defect detection algorithm based on the RT-DETR model.Firstly,to boost detection accuracy,the FREBlock architecture is developed,which not only improves feature extraction but also enhances detection efficiency.Secondly,the CRDFP multi-scale feature fusion structure is designed to strengthen the integration of features across different scales.Lastly,the deformable attention mechanism is incorporated,which enables the model to focus on the information features of the region of interest.The experimental results indicate that the improved model achieves an average mean Average Precision(mAP)of 79.2％,an increase of 3.6 percentage points over the traditional model.Additionally,the model's parameters reduces 22.6％,and the computational load decreases 25.9％,demonstrating a high capacity for real-time detection.关键词
深度学习/RT-DETR/光伏板/缺陷检测/多尺度特征融合Key words
deep learning/RT-DETR/photovoltaic panels/defect detection/multi-scale feature fusion分类
信息技术与安全科学引用本文复制引用
吕辉,司可..基于改进RT-DETR的光伏板缺陷检测[J].广西师范大学学报（自然科学版）,2026,44(2):52-64,13.基金项目
河南省自然科学基金(242300420283) （242300420283）
河南省高校基本科研业务费专项资金资助(NSFRF240819) （NSFRF240819）
