PHOTOVOLTAIC MODULE DEFECT DETECTION METHOD BASED ON IMPROVED YOLOV5S NETWORK

Ren Xiwei; Yu Jie; Han Xin; Li Zhaoyun; Yang Menglu; He Lifeng

doi:10.19912/j.0254-0096.tynxb.2023-1934

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PHOTOVOLTAIC MODULE DEFECT DETECTION METHOD BASED ON IMPROVED YOLOV5S NETWORK

更新时间：2025-10-18

- PHOTOVOLTAIC MODULE DEFECT DETECTION METHOD BASED ON IMPROVED YOLOV5S NETWORK
- Vol. 46, Issue 3, Pages: 428-434(2025)
- 作者机构：
  
  1. 陕西科技大学电子信息与人工智能学院,西安 710021
  2. 陕西省人工智能实验室（陕西科技大学）,西安 710021
- 作者简介：
- 基金信息：
- DOI：10.19912/j.0254-0096.tynxb.2023-1934
  CLC： TK511TP391.4
- Published Online：07 April 2025，
  
  Published：2025
- 稿件说明：
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任喜伟, 余杰, 韩欣, 李兆允, 杨梦璐, 何立风. 基于改进YOLOv5s网络的光伏组件缺陷检测方法[J]. 太阳能学报, 2025, 46(3): 428-434.

Ren Xiwei, Yu Jie, Han Xin, et al. PHOTOVOLTAIC MODULE DEFECT DETECTION METHOD BASED ON IMPROVED YOLOV5S NETWORK[J]. 2025, 46(3): 428-434.
任喜伟, 余杰, 韩欣, 李兆允, 杨梦璐, 何立风. 基于改进YOLOv5s网络的光伏组件缺陷检测方法[J]. 太阳能学报, 2025, 46(3): 428-434. DOI： 10.19912/j.0254-0096.tynxb.2023-1934.

Ren Xiwei, Yu Jie, Han Xin, et al. PHOTOVOLTAIC MODULE DEFECT DETECTION METHOD BASED ON IMPROVED YOLOV5S NETWORK[J]. 2025, 46(3): 428-434. DOI： 10.19912/j.0254-0096.tynxb.2023-1934.

摘要

鉴于光伏组件排列密集

缺陷目标较小

难以检测维护

直接影响光伏组件发电效率

提出一种改进YOLOv5s网络的光伏组件缺陷检测方法。首先

针对光伏组件红外图像中缺陷尺寸特性

使用K-均值++算法对缺陷目标重新聚类

确定合适的锚框大小

使其聚类锚框更符合小目标特征;然后

使用简化BiFPN融合更多的特征

在融合之前添加一个多通路残差连接模块

提高对小目标光伏缺陷的敏感度;其次

将YOLOv5s骨干网络进行融合

简化网络结构

减少下采样次数

提高图像分辨率以及丰富小目标特征信息;之后

将S

IoU

损失函数引入到YOLOv5s架构中

提高网络性能

让网络部署到小型、轻量化设备;最后

将改进的YOLOv5s网络在自建的光伏组件红外图像缺陷数据集进行测试。实验结果表明

改进的YOLOv5s网络光伏组件缺陷检测方法优于对比方法

相比于原始网络

改进网络的m

AP@0.5

提高1.7%

每秒帧率达到46.3

完全满足光伏组件缺陷检测过程中的实际需求。

Abstract

Given the dense arrangement and small target size of photovoltaic modules

which make defect detection and maintenance challenging and directly impact the power generation efficiency of photovoltaic modules

an improved method for detecting defects in photovoltaic modules using the YOLOv5s network is proposed. Firstly

considering the size charact eristics of defects in infrared images of photovoltaic modules

the K-means++ algorithm is used to re-cluster defect targets

determining appropriate anchor box sizes to ensure that the clustered anchor boxes better fit the characteristics of small targets. Then

a simplified BiFPN is employed to fuse more feat

ures

with a multi-path residual connection module added before fusion to enhance sensitivity to small target photovoltaic defects. Furthermore

the YOLOv5s backbone network is merged

simplifying the network structure

reducing the number of downsampling operations

increasing image resolution

and enriching information on small target features. Subsequently

the S

IoU

loss function is introduced into the YOLOv5s architecture to improve network performance

enabling deployment on small

lightweight devices. Finally

the improved YOLOv5s network is tested on a self-built dataset of infrared images of photovoltaic module defects. Experimental results demonstrate that the improved YOLOv5s network for photovoltaic module defect detection outperforms the baseline methods. Compared to the original network

the improved network’s m

AP@0.5

increased by 1.7%

reaching an FPS of 46.3

fully meeting the practical requirements of photovoltaic module defect detection.

关键词

Keywords

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