DEFECT DETECTION OF PHOTOVOLTAIC MODULES BASED ON MULTI-SCALE FEATURE FUSION SSDLite

Xiang Xinjian; Tang Hui; Xiao Jiale; Wang Shiqian; Zhang Yingchao; Wang Lei

doi:10.19912/j.0254-0096.tynxb.2023-1544

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (1) : 669-675. DOI: 10.19912/j.0254-0096.tynxb.2023-1544

DEFECT DETECTION OF PHOTOVOLTAIC MODULES BASED ON MULTI-SCALE FEATURE FUSION SSDLite

Xiang Xinjian¹, Tang Hui¹, Xiao Jiale¹, Wang Shiqian¹, Zhang Yingchao¹, Wang Lei²

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Abstract

To address the issues of slow manual inspection and the high hardware costs and slower speeds associated with using deep learning models like YOLO in photovoltaic module defect detection, a lightweight object detection method based on SSDLite with multi-level feature fusion is proposed. This method employs MobileNetV2 as the backbone network of the SSDLite model and extracts three different feature layers for feature fusion. The sizes of the anchor boxes in the model are redesigned based on the size characteristics of different defects. Additionally, the CBAM attention mechanism is introduced into the bottleneck structure of MobileNetV2 to enhance the detection accuracy of the model. Compared to the traditional SSDLite model, the proposed model improves the mean average precision （mAP） from 65.8% to 72.4%. Although the speed slightly decreases, it still largely meets the requirements of practical applications.

Key words

photovoltaic modules / object detection / deep learning / SSDLite / multi-layer feature fusion / MobileNetV2

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Xiang Xinjian, Tang Hui, Xiao Jiale, Wang Shiqian, Zhang Yingchao, Wang Lei. DEFECT DETECTION OF PHOTOVOLTAIC MODULES BASED ON MULTI-SCALE FEATURE FUSION SSDLite[J]. Acta Energiae Solaris Sinica. 2025, 46(1): 669-675 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1544

References

[1] REDMON J, DIVVALA S, GIRSHICK R, et al.You only look once: unified, real-time object detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA, 2016: 779-788.
[2] REDMON J, FARHADI A.YOLO9000: better, faster, stronger[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA, 2017: 6517-6525.
[3] BOCHKOVSKIY A, WANG C Y, LIAO H M. YOLOv4: optimal speed and accuracy of object detection[EB/OL].2020: 2004. 10934. https://arxiv.org/abs/2004.10934v1.
[4] LIU W, ANGUELOV D, ERHAN D, et al.SSD: single shot MultiBox detector[M]//Lecture Notes in Computer Science. Cham: Springer International Publishing, 2016: 21-37.
[5] FU C Y, LIU W, RANGA A, et al. DSSD: deconvolutional single shot detector[EB/OL].2017: 1701.06659. https://arxiv.org/abs/1701.06659v1.
[6] JEONG J, PARK H, KWAK N. Enhancement of SSD by concatenating feature maps for object detection[EB/OL].2017: 1705.09587. https://arxiv.org/abs/1705.09587v1.
[7] GIRSHICK R.Fast R-CNN[C]//2015 IEEE International Conference on Computer Vision (ICCV). Santiago, Chile, 2015: 1440-1448.
[8] 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, 2017, 39(6): 1137-1149.
[9] 王道累, 李明山, 姚勇, 等. 改进SSD的光伏组件热斑缺陷检测方法[J]. 太阳能学报, 2023, 44(4): 420-425.
WANG D L, LI M S, YAO Y, et al.Method of hotspot detection of photovoltaic panels modules on improved ssd[J]. Acta energiae solaris sinica, 2023, 44(4): 420-425.
[10] 孙建波, 王丽杰, 麻吉辉, 等. 基于改进YOLOv5s算法的光伏组件故障检测[J]. 红外技术, 2023, 45(2): 202-208.
SUN J B, WANG L J, MA J H, et al.Photovoltaic module fault detection based on improved YOLOv5s algorithm[J]. Infrared technology, 2023, 45(2): 202-208.
[11] 郭岚,刘正新.基于改进YOLOv5的光伏组件缺陷检测[J]. 激光与光电子学进展, 2023, 60(20): 148-156.
GUO L, LIU Z X.Detection of defects in photovoltaic modules by improved YOLOv5[J]. Laser & Optoelectronics Progress, 2023, 60(20): 148-156.
[12] SANDLER M, HOWARD A, ZHU M L, et al.MobileNetV2: inverted residuals and linear bottlenecks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA, 2018: 4510-4520.
[13] CHOLLET F.Xception: deep learning with depthwise separable convolutions[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA, 2017: 1800-1807.
[14] HOWARD A G, ZHU M L, CHEN B, et al. MobileNets: efficient convolutional neural networks for mobile vision applications[EB/OL].2017: 1704.04861. https://arxiv.org/abs/1704.04861v1
[15] WOO S, PARK J, LEE J Y, et al.CBAM: convolutional block attention module[M]. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2018: 3-19.
[16] HU J, SHEN L, SUN G.Squeeze-and-excitation networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA, 2018: 7132-7141.