SOLAR CELL DEFECT DETECTION NETWORK BASED ON MULTI-SCALE ASYMPTOTIC PYRAMID

Zhu Lei; Geng Cuicui; Li Botao; Pan Yang; Zhang Bo; Yao Li&#x02019;na

doi:10.19912/j.0254-0096.tynxb.2024-0064

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (5) : 267-274. DOI: 10.19912/j.0254-0096.tynxb.2024-0064

SOLAR CELL DEFECT DETECTION NETWORK BASED ON MULTI-SCALE ASYMPTOTIC PYRAMID

Zhu Lei, Geng Cuicui, Li Botao, Pan Yang, Zhang Bo, Yao Li’na

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Abstract

A multi-scale asymptotic pyramid network called MSANet is proposed based on the YOLOv8 network. Initially, we replace conventional convolution layers with feature extraction blocks （M-Block） containing a hierarchical feature fusion structure to enhance the network's capability multi-scale feature extraction. Subsequently, we introduce the spatial attention mechanism SRU to suppress feature redundancy in background regions, allowing the network to focus more on crucial areas while reducing the introduction of parameters. Finally, we propose an improved asymptotic pyramid network structure, AFPNa, to mitigate information loss or degradation during the feature fusion process, thereby enhancing defect detection accuracy. Experimental results demonstrate that compared to the original YOLOv8 model and seven other advanced detection networks, including RTMDET, MSANet achieves higher detection accuracy, with a 5.7% improvement in mean average precision compared to the original model.

Key words

defect detection / deep learning / solar cells / hierarchical feature fusion structure / multi-scale asymptotic pyramid / spatial attention mechanism

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Zhu Lei, Geng Cuicui, Li Botao, Pan Yang, Zhang Bo, Yao Li’na. SOLAR CELL DEFECT DETECTION NETWORK BASED ON MULTI-SCALE ASYMPTOTIC PYRAMID[J]. Acta Energiae Solaris Sinica. 2025, 46(5): 267-274 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0064

References

[1] 周颖, 王如意, 袁梓桐, 等. 一种高效双路径注意力太阳电池缺陷检测网络[J]. 太阳能学报, 2023, 44(4): 407-413.
ZHOU Y, WANG R Y, YUAN Z T, et al.An efficient dual-path attention solar cell defect detection network[J]. Acta energiae solaris sinica, 2023, 44(4): 407-413.
[2] 张治, 邹鹏辉, 刘志锋, 等. 发射极掺杂工艺对产业化IBC太阳电池性能的影响[J]. 太阳能学报, 2022, 43(3): 158-162.
ZHANG Z, ZOU P H, LIU Z F, et al.Influence of emitter doping process on performance of industrial IBC solar cell[J]. Acta energiae solaris sinica, 2022, 43(3): 158-162.
[3] WANG Y L, HOU T, ZHANG X, et al.Surface defect detection of solar cell based on similarity non-maximum suppression mechanism[J]. Signal, image and video processing, 2023, 17(5): 2583-2593.
[4] 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.
[5] PENG T, WEN F.Photovoltaic panel fault detection based on improved mask R-CNN[C]//2023 IEEE International Conference on Control, Electronics and Computer Technology (ICCECT). Jilin, China, 2023: 1187-1191.
[6] XU M Y.Solar cell defect detection based on improved G-SSD Network[J]. International journal of energy, 2023, 2(1): 68-71.
[7] 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.
[8] CHEN Y M, YUAN X B, WU R Q, et al. YOLO-MS: rethinking multi-scale representation learning for real-time object detection[EB/OL].2023: 2308.05480.https://arxiv.org/abs/2308.05480v1.
[9] LI J F, WEN Y, HE L H.SCConv: spatial and channel reconstruction convolution for feature redundancy[C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Vancouver, BC, Canada, 2023: 6153-6162.
[10] YANG G Y, LEI J, ZHU Z K, et al.AFPN: asymptotic feature pyramid network for object detection[C]//2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Honolulu, Oahu, HI, USA, 2023: 2184-2189.
[11] 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, 2023, 19(1): 404-413.
[12] 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.
[13] TIAN Z, SHEN C H, CHEN H, et al.FCOS: fully convolutional one-stage object detection[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul, Korea (South), 2019: 9626-9635.
[14] WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Vancouver, BC, Canada, 2023: 7464-7475.
[15] LYU C Q, ZHANG W W, HUANG H A, et al. RTMDet: an empirical study of designing real-time object detectors[EB/OL].2022: 2212.07784.https://arxiv.org/abs/2212.07784v2.
[16] ZHAO Y A, LYU W Y, XU S L, et al. DETRs beat YOLOs on real-time object detection[EB/OL].2023: 2304.08069.https://arxiv.org/abs/2304.08069v3.
[17] SELVARAJU R R, COGSWELL M, DAS A, et al.Grad-CAM: visual explanations from deep networks via gradient-based localization[J]. International journal of computer vision, 2020, 128(2): 336-359.