广西师范大学学报（自然科学版）2026，Vol.44Issue(3)：47-59,13.DOI:10.16088/j.issn.1001-6600.2025071102
基于改进YOLO11n的光伏板缺陷检测方法
Photovoltaic Panel Defect Detection Method Based on Improved YOLO11n
摘要
Abstract
To address the issues of weakened features for distant small-target defects and high model complexity in photovoltaic panel defect detection algorithms,this study proposes an improved lightweight algorithm named FEM-YOLO.Firstly,the C3k2 module is enhanced by integrating FasterBlock and EMA,constructing a C3k2-Faster-EMA structure to improve the network's ability to learn and capture features of defective targets.Subsequently,the Mona module is incorporated into the C2PSA block,optimizing the model's feature extraction and representation capabilities.Moreover,the MLCA mechanism is integrated into the backbone network to enhance the robustness of feature extraction for diverse targets.Finally,an additional P2 detection layer is added specifically for small targets,and an efficient detection head named EfficientHead is designed.This combination enhances the capability to capture micro-defects while simultaneously reducing model complexity.Experimental results demonstrate that,compared with the original YOLO11n model,the improved algorithm achieves increases of 1.9%in both mAP50 and mAP50-95 metrics.Furthermore,the model parameter count is reduced to 2.1×106 and the model size is compressed to 4.4 MiB.Thus,the proposed FEM-YOLO algorithm significantly enhances detection accuracy while substantially reducing model complexity.关键词
光伏板/缺陷检测/轻量化模型/小目标检测/YOLO11nKey words
photovoltaic panel/defect detection/lightweight model/small object detection/YOLO11n分类
信息技术与安全科学引用本文复制引用
杨云波,南新元,蔡鑫..基于改进YOLO11n的光伏板缺陷检测方法[J].广西师范大学学报（自然科学版）,2026,44(3):47-59,13.基金项目
国家自然科学基金(62303394) （62303394）
天山英才-青年拔尖人才项目(2024TSYCCX0011) （2024TSYCCX0011）
新疆维吾尔自治区高校基本科研业务费(XJEDU2023P025) （XJEDU2023P025）
