计算机工程与应用2025，Vol.61Issue(14)：123-134,12.DOI:10.3778/j.issn.1002-8331.2412-0381
基于改进YOLOv11n的光伏板异物与缺陷检测模型研究
Research on Detection Model of Foreign Objects and Defects in Photovoltaic Panels Based on Improved YOLOv11n
韩涛 1于帅帅 1马玲 2黄友锐 1侯帅男 3庞家乐3
作者信息
- 1. 安徽理工大学 电气与信息工程学院,安徽 淮南 232001
- 2. 安徽大学 电气与自动化学院,合肥 230601
- 3. 安徽理工大学 计算机科学与工程学院,安徽 淮南 232001
- 折叠
摘要
Abstract
To address the issues of low detection accuracy and high computational resource demands of YOLOv11n in photovoltaic panel foreign object and defect detection,this paper proposes an improved YOLOv11n-based algorithm named FESI-YOLOv11n.Firstly,the C3k2 module is replaced with the C3k2_Faster_EMA module,which expands the initial convolutional channels to enable more efficient multi-scale feature extraction.Secondly,a reconstructed detection head is proposed,integrating multi-branch and multi-scale design with re-parameterization to enhance the feature extrac-tion capability of single convolutions.Thirdly,an SEAttention mechanism is added before feature fusion to reduce compu-tational overhead.Finally,the Inner_DIoU loss function replaces the CIoU loss to address limitations in bounding box regression and further improve detection performance.Experimental results demonstrate that compared to the original YOLOv11n model,the improved algorithm achieves a 3.6 percentage points increase in mAP50 and a 3.4 percentage points improvement in mAP50-95,while reducing model parameters by 21.29%and computational load by 25.4%.These advancements validate the superior applicability of the proposed algorithm in detecting foreign objects and defects in photovoltaic panels.关键词
光伏板/YOLOv11n/异物检测/缺陷检测Key words
solar photovoltaic panels/YOLOv11n/foreign object detection/defect detection分类
能源科技引用本文复制引用
韩涛,于帅帅,马玲,黄友锐,侯帅男,庞家乐..基于改进YOLOv11n的光伏板异物与缺陷检测模型研究[J].计算机工程与应用,2025,61(14):123-134,12.
