Defect Detection of Photovoltaic Panel Based on Improved YOLOv8-EDD

doi:10.3969/j.issn.1006-2475.2025.09.006

Abstract

Abstract:
Abstract： To solve the problems of low accuracy and slow detection speed of existing defect detection methods for photovoltaic panels， an novel defect detection model for photovoltaic panels is proposed based on improved YOLOv8-EDD. Firstly， multi-scale attention mechanism EMA is introduced to enable YOLOv8 model to pay more attention to the defect area of photovoltaic panels. Secondly， deformable convolutional DCNv2 is embedded into the original C2f module to enhance the model’s ability to extract irregular defect shape. At the same time， in order to alleviate the problem of reduced model detection speed due to the large number of DCNv2 parameters， the DySample lightweight upsampling operator is used to replace the original upsampling operator of YOLOv8 to reduce the number of model parameters and calculation complexity， thus to enhance the detecting speed. Finally， WIoUv3 loss function is integrated to reduce the influence of low-quality samples on the accuracy and improve the generalization ability of the model. In the experiment， compared with the original model， the accuracy of the improved YOLOv8-EDD model increases by 15.3 percentage points， the recall rate increases by 11.3 percentage points， mean of the average accuracy increases by 10.5 percentage points， and the detection speed has increased by 6.5 FPS. The results show that the proposed model not only improves the detection accuracy but also has faster detection speed， and is more suitable for the defect detection of photovoltaic panels.

Key words: Key words： YOLOv8, photovoltaic panel defect detection, EMA, DySample, WIoUv3

CLC Number:

中图分类号：TP391.41

JIA Tao1, WU Yuechao2, LYU Yang2, FU Wenlong3. Defect Detection of Photovoltaic Panel Based on Improved YOLOv8-EDD[J]. Computer and Modernization, 2025, 0(09): 43-49.

References

［1］ MAZEN F M A， SEOUD R A A， SHAKER Y O. Deep learning for automatic defect detection in PV modules using electroluminescence images［J］. IEEE Access， 2023，11:57783-57795.
［2］ WANG Y， ZHAO J Y， YAN Y H， et al. Pushing the boundaries of solar panel inspection: Elevated defect detection with YOLOv7-GX technology［J］. Electronics， 2024，13（8）. DOI: 10.3390/electronics13081467.
［3］ HUANG J， ZENG K Y， ZHANG Z J， et al. Solar panel defect detection design based on YOLOv5 algorithm［J］. Heliyon， 2023，9（8）. DOI：10.1016/j.heliyon.2023.e18826.
［4］林永君，张世成，杨凯，等. 基于时钟循环神经网络的光伏故障诊断［J］. 山东电力技术， 2024，51（1）:52-58.
［5］耿洪彬，张英杰，魏燕飞，等. 基于多源数据的光伏电站在线热斑检测性能评价系统研究［J］. 山东电力技术， 2023，50（1）:14-19.
［6］朱成杰，刘乐乐，朱洪波. 多尺度的YOLOv8-MNS光伏板缺陷检测算法［J/OL］. 重庆工商大学学报（自然科学版）:1-9［2024-09-16］. https://wenku.csdn.net/answer/hqor268i4m.
［7］ ESQUIVEL O. Contrast Imaging Method for Inspecting Specular Surface Devices: U.S. Patent 6，433，867［P］. 2002-08-13.
［8］ KANG B K， KIM S T， BAE S H， et al. Diagnosis of output power lowering in a PV array by using the Kalman-filter algorithm［J］. IEEE Transactions on Energy Conversion， 2012，27（4）:885-894.
［9］曹燕，李欢，王天宝. 基于深度学习的目标检测算法研究综述［J］. 计算机与现代化， 2020（5）:63-69.
［10］ HE K M， GKIOXARI G， DOLLÁR P， et al. Mask R-CNN［C］// Proceedings of 2017 IEEE International Conference on Computer Vision. IEEE， 2017:2961-2969.
［11］ REN S Q， HE K M， GIRSHICK R， et al. Faster R-CNN: Towards real-time object detection with region proposal networks［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2016，39（6）:1137-1149.
［12］鲁东林，王淑青，鲁濠，等. 一种改进Faster R-CNN的太阳能电池片缺陷检测方法［J］. 激光杂志， 2022，43（3）:50-55.
［13］ LIU W， ANGUELOV D， ERHAN D， et al. SSD: Single shot multibox detector［C］// Proceedings of the 14th European Conference on Computer Vision-ECCV 2016. Springer， 2016:21-37.
［14］ REDMON J， DIVVALA S， GIRSHICK R， et al. You only look once: Unified， real-time object detection［C］// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. IEEE， 2016:779-788.
［15］焦思韬，王可庆，周奇，等. 基于改进SSD算法的光伏板缺陷检测研究［J］. 软件， 2023，44（12）:47-52.
［16］孔松涛，徐甄泽，林星宇，等. 基于改进YOLOv5算法的光伏组件红外热成像缺陷检测［J］. 红外技术， 2023，45（9）:974-981.
［17］周启宸，王伯超. 基于改进YOLOv7的太阳能电池片表面缺陷检测［J］. 计算机应用， 2023，43（增2）:223-228.
［18］ NIU S， XU X， LIANG A， et al. Research on a lightweight method for maize seed quality detection based on improved YOLOv8［J］. IEEE Access， 2024，12:32927-32937.
［19］ SHEN L， LANG B， SONG Z. Infrared object detection method based on DBD-YOLOv8［J］. IEEE Access， 2023， 11:145853-145868.
［20］ LIU M X， LI R X， HOU M X， et al. SD-YOLOv8: An accurate seriola dumerili detection model based on improved YOLOv8［J］. Sensors， 2024，24（11）. DOI：10.3390/s24113647.
［21］ OUYANG D L， HE S， ZHANG G L， et al. Efficient multi-scale attention module with cross-spatial learning［C］// 2023 IEEE International Conference on Acoustics， Speech and Signal Processing （ICASSP）. IEEE， 2023:1-5.
［22］ ZHU X Z， HU H， LIN S， et al. Deformable convnets v2: More deformable， better results［C］// Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE， 2019:9308-9316.
［23］ LIU W Z， LU H， FU H T， et al. Learning to upsample by learning to sample［C］// Proceedings of 2023 IEEE/CVF International Conference on Computer Vision. IEEE， 2023:6027-6037.
［24］ TONG Z J， CHEN Y H， XU Z W， et al. Wise-IoU: Bounding box regression loss with dynamic focusing mechanism［J］. arXiv preprint arXiv:2301.10051， 2023.
［25］雷帮军，余翱，余快. 基于YOLOv8s改进的小目标检测算法［J］. 无线电工程， 2024，54（4）:857-870.
［26］熊恩杰，张荣芬，刘宇红，等. 面向交通标志的Ghost-YOLOv8检测算法［J］. 计算机工程与应用， 2023，59（20）:200-207.
［27］ SU B Y， ZHOU Z， CHEN H Y. PVEL-AD: A large-scale open-world dataset for photovoltaic cell anomaly detection［J］. IEEE Transactions on Industrial Informatics， 2022，19（1）:404-413.
［28］ WANG J Q， CHEN K， XU R， et al. CARAFE: Content-aware reassembly of features［C］// Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. IEEE， 2019:3007-3016.

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