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基于改进YOLO v5s算法的光伏组件故障检测

孙建波 王丽杰 麻吉辉 高玮

孙建波, 王丽杰, 麻吉辉, 高玮. 基于改进YOLO v5s算法的光伏组件故障检测[J]. 红外技术, 2023, 45(2): 202-208.
引用本文: 孙建波, 王丽杰, 麻吉辉, 高玮. 基于改进YOLO v5s算法的光伏组件故障检测[J]. 红外技术, 2023, 45(2): 202-208.
SUN Jianbo, WANG Lijie, MA Jihui, GAO Wei. Photovoltaic Module Fault Detection Based on Improved YOLOv5s Algorithm[J]. Infrared Technology , 2023, 45(2): 202-208.
Citation: SUN Jianbo, WANG Lijie, MA Jihui, GAO Wei. Photovoltaic Module Fault Detection Based on Improved YOLOv5s Algorithm[J]. Infrared Technology , 2023, 45(2): 202-208.

基于改进YOLO v5s算法的光伏组件故障检测

基金项目: 

国家自然科学基金项目 61975047

详细信息
    作者简介:

    孙建波(1995-),男,硕士研究生,主要研究方向为计算机视觉、目标检测。E-mail: 987277295@qq.com

    通讯作者:

    王丽杰(1971-),女,教授,硕士生导师。主要研究方向为图像分析与识别等。E-mail: wlj@hrbust.edu.cn

  • 中图分类号: TP181

Photovoltaic Module Fault Detection Based on Improved YOLOv5s Algorithm

  • 摘要: 针对无人机在光伏组件巡检任务中红外故障图像识别准确率低、检测速度慢的问题,提出一种特征增强的YOLO v5s故障检测算法。首先对损失函数进行优化,将原有的回归损失计算方法由GIOU(generalized intersection over union)改为功能更加强大的EIOU(efficient intersection over union)损失函数,并自适应调节置信度损失平衡系数,提升模型训练效果;随后,在每个检测层前分别添加InRe特征增强模块,通过丰富特征表达增强目标特征提取能力。最后,用创建的红外光伏数据集进行对比验证。实验结果表明:本文方法均值平均精度(mean average precision, mAP)为92.76%,检测速度(frame per second,FPS)达到42.37 FPS,其中热斑、组件脱落两种故障类型平均精度分别为94.85%、90.67%,完全能够满足无人机自动巡检的需求。
  • 图  1  两种常见光伏组件故障类型样例

    Figure  1.  Examples of two common PV module failure types

    图  2  YOLO v5s结构示意图

    Figure  2.  Structural diagram of YOLO v5s

    图  3  GIOU示意图

    Figure  3.  Schematic diagram of GIOU

    图  4  InRe特征增强模块

    Figure  4.  InRe feature enhancement module

    图  5  改进后的YOLO v5s模型

    Figure  5.  Improved YOLO v5s model

    图  6  损失变化

    Figure  6.  Loss diagram

    图  7  实验六损失函数变化

    Figure  7.  Loss diagram of experiment 6

    图  8  检测结果对比

    Figure  8.  Comparison of test results

    表  1  消融实验

    Table  1.   Ablation experiment

    Experiment EIOU Balance parameter InRe mAP% FPS
    Experiment 1 - - - 85.57 44.62
    Experiment 2 - - 88.67 46.91
    Experiment 3 - - 85.97 46.93
    Experiment 4 - 88.54 47.50
    Experiment 5 - - 89.81 40.82
    Experiment 6 92.76 42.37
    下载: 导出CSV

    表  2  对比实验

    Table  2.   Comparative experiment

    Algorithm AP/% Parameter quantity (M) mAP/% FPS
    Damage Hot spot Model size/MB
    Faster-RCNN 92.45 87.95 522.91 137.08 90.20 16.33
    SSD 90.95 78.37 90.58 23.75 84.66 36.50
    YOLOv3 92.31 88.05 236.32 61.95 90.18 41.31
    YOLOv4 91.08 85.76 243.92 63.94 88.42 25.87
    YOLOv5s 90.36 78.06 26.96 7.07 84.21 44.56
    YOLOv4-mobileNetv2 92.24 87.62 46.11 12.15 89.93 38.26
    YOLOv4-tiny 76.19 54.89 22.42 5.88 65.54 56.18
    YOLOv5-mobileNet 77.44 60.21 7.42 2.82 68.83 43.58
    Proposed 94.85 90.67 30.52 7.41 92.76 42.37
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-05-23
  • 修回日期:  2022-06-24
  • 刊出日期:  2023-02-20

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