针对光伏组件电致发光（EL）图像中缺陷因复杂背景干扰而难以识别的问题,提出一种基于YOLOv5改进的深度学习模型YOLOv5-PMD,有助于提高光伏组件EL图像缺陷检测的准确性。首先使用轻量级卷积GSConv模块替换颈部网络（Neck）的卷积Conv模块,增强模型准确性的同时可减少模型参数量。其次引入卷积块注意力模块（CBAM）,增强模型对特征的感知能力。最后将原模型的CIoU损失函数替换为EIoU损失函数,更加准确地衡量缺陷的位置。在光伏组件EL图像的公开SolarMonocrystal数据集上,改进模型的训练参数量从7.025×106降到6.695×106,平均精度均值mAP50为89.8%,相较于原模型提高2.6％。
Abstract
The complex background interference in electroluminescence（EL） images of photovoltaic modules poses challenges for accurate defect identification. To address this, an improved deep learning model YOLOV5-PMD based on YOLOv5 is proposed. This model utilizes a lightweight convolution GSConv module to replace the Conv part of the Neck, enhancing accuracy while reducing the number of parameters. In addition, the convolutional CBAM attention mechanism is combined to improve the feature perception ability, and the EIoU loss function is replaced by the CIoU loss function to achieve more accurate defect location. On the public SolarMonocrystal dataset, the improved model reduces the training parameters from 7.025×106 to 6.695×106, and the average accuracy of mAP50 reaches 89.8%, which is 2.6% higher than that of the original model.
关键词
光伏组件 /
缺陷 /
深度学习 /
注意力机制 /
YOLOv5 /
损失函数
Key words
photovoltaic modules /
defect /
deep learning /
attention mechanism /
YOLOv5 /
loss function
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 李明东, 李婧雯. “双碳” 目标下中国分布式光伏发电的发展现状和展望[J]. 太阳能, 2023(5): 5-10.
LI M D, LI J W.Development status and prospects of distributed PV power generation in China under the goal of emission peak and carbon neutrality[J]. Solar energy, 2023(5): 5-10.
[2] 徐惠. 浅析太阳能光伏组件隐裂的影响及控制措施[J]. 安装, 2020(8): 70-72.
XU H.Analysis of the impact of hidden cracks in solar photovoltaic modules and its control measures[J]. Installation, 2020(8): 70-72.
[3] 蒋尚俊, 易辉, 李红涛, 等. 基于迁移学习与ResNet的太阳电池缺陷检测方法[J]. 太阳能学报, 2023, 44(7): 116-121.
JIANG S J, YI H, LI H T, et al.Defect detection method of solar cells based on transfer learning and ResNet[J]. Acta energiae solaris sinica, 2023, 44(7): 116-121.
[4] 张德钰. 基于YOLOv5的太阳能电池片EL图像缺陷检测算法[J]. 电视技术, 2024, 48(1): 42-47, 55.
ZHANG D Y.Solar cells EL image defect detection and classification based on improved YOLOv5[J]. Video engineering, 2024, 48(1): 42-47, 55.
[5] 谢林森, 朱文忠, 谢康康, 等. 基于改进YOLOv5s的光伏电池EL缺陷图像检测模型[J]. 国外电子测量技术, 2023, 42(6): 93-102.
XIE L S, ZHU W Z, XIE K K, et al.Improved YOLOv5s-based image detection model for EL defects in photovoltaic cells[J]. Foreign electronic measurement technology, 2023, 42(6): 93-102.
[6] LIANG L, WANG X B, LI X F, et al.Research on fault object detection method for photovoltaic panel UAV inspection based on YOLOv5[C]//2023 9th International Conference on Computer and Communications(ICCC). Chengdu, China, 2023: 1766-1770.
[7] CONG Z H, SUN H K, WANG S J, et al.YOLOv5-CPP: improved YOLOv5-based defect detection for photovoltaic panels[C]//2023 42nd Chinese Control Conference (CCC), Tianjin, China, 2023: 8294-8299.
[8] 范钧玮, 饶全瑞, 赵薇, 等. 改进的YOLOv5双影像光伏故障小目标检测[J]. 太阳能学报, 2024, 45(7): 510-516.
FAN J W, RAO Q R, ZHAO W, et al.Improved YOLOv5 dual-image photovoltaic fault small target detection[J]. Acta energiae solaris sinica, 2024, 45(7): 510-516.
[9] 徐威, 李为相, 方志, 等. 基于改进YOLOv7-tiny的光伏电池缺陷检测算法[J]. 计算机工程与应用, 2024, 60(15): 336-343.
XU W, LI W X, FANG Z, et al.Solar cell defect detection algorithm based on improved YOLOv7-tiny[J]. Computer engineering and applications, 2024, 60(15): 336-343.
[10] GUAN Q J, YU K, WANG H R, et al.Lightweight protective clothing detection algorithm based on ghost convolution and GSConv convolution[C]//2023 International Conference on the Cognitive Computing and Complex Data (ICCD). Huaian, China, 2023: 73-77.
[11] 梁礼明, 龙鹏威, 卢宝贺, 等. 改进GBS-YOLOv7t的钢材表面缺陷检测[J]. 光电工程, 2024, 51(5): 240044.
LIANG L M, LONG P W, LU B H, et al.Improvement of GBS-YOLOv7t for steel surface defect detection[J]. Opto-electronic engineering, 2024, 51(5): 240044.
[12] 邓志军, 田秋红. 改进Inception-v3网络的手势图像识别[J]. 计算机系统应用, 2022, 31(11): 157-166.
DENG Z J, TIAN Q H.Improved Inception-v3 network for gesture image recognition[J]. Computer systems and applications, 2022, 31(11): 157-166.
[13] 陈辉, 张傲, 孙帅, 等. 基于改进DeepLabv3+的含噪声热红外图像光伏热斑检测方法[J]. 太阳能学报, 2023, 44(11): 23-30.
CHEN H, ZHANG A, SUN S, et al.Photovoltaic thermal spot detection method with noisy thermal infrared image based on improved DeepLabv3+[J]. Acta energiae solaris sinica, 2023, 44(11): 23-30.
[14] 王向前, 成高立, 胡鹏, 等. 基于改进YOLOv5的路面裂缝检测方法[J]. 电子技术应用, 2024, 50(3): 80-85.
WANG X Q, CHENG G L, HU P, et al.Pavement crack detection method based on improved YOLOv5[J]. Application of electronic technique, 2024, 50(3): 80-85.
[15] 王银, 高瑞泽, 李茂环, 等. 基于YOLOv5 LiteX算法的光伏组件缺陷检测[J]. 太阳能学报, 2023, 44(9): 101-108.
WANG Y, GAO R Z, LI M H, et al.Photovoltaic modules defect detection based on YOLOv5 LiteX algorithm[J]. Acta energiae solaris sinica, 2023, 44(9): 101-108.
[16] 李安达, 吴瑞明, 李旭东. 改进YOLOv7的小目标检测算法研究[J]. 计算机工程与应用, 2024, 60(1): 122-134.
LI A D, WU R M, LI X D.Research on improving YOLOv7’s small target detection algorithm[J]. Computer engineering and applications, 2024, 60(1): 122-134.
基金
郑州航空工业管理学院科研团队支持计划专项资助（23ZHTD01006）
PDF(1132 KB)
