A HOTSPOT DETECTION METHOD FOR INFRARED IMAGES OF AERIAL PHOTOVOLTAIC MODULES BASED ON CSWIN

Wang Wei; Zhao Kuan; Yang Yaoquan; Zhai Yongjie

doi:10.19912/j.0254-0096.tynxb.2022-0846

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (10) : 142-147. DOI: 10.19912/j.0254-0096.tynxb.2022-0846

A HOTSPOT DETECTION METHOD FOR INFRARED IMAGES OF AERIAL PHOTOVOLTAIC MODULES BASED ON CSWIN

Wang Wei, Zhao Kuan, Yang Yaoquan, Zhai Yongjie

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Abstract

Aiming at the problems of low contrast, complex background and difficult detection of small hot spots in aerial photovoltaic infrared images, a cross-stage partial network incorporating Swin transformer （CSwin） is proposed to reduce the number of parameters and capture the global position and spatial information of the image. The multi-scale feature path aggregation network based on CSwin （MPC） is constructed to strengthen the information interaction of multi-scale features and further improve the small target detection ability. The qualitative and quantitative experiments are carried out on the aerial photovoltaic infrared image data set, and the effectiveness of the method in aerial photovoltaic infrared image hot spot detection in proved.

Key words

PV modules / object detection / deep learning / YOLOv5 / multi-scale feature fusion / infrared image / hot spot detection / Swin transformer

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Wang Wei, Zhao Kuan, Yang Yaoquan, Zhai Yongjie. A HOTSPOT DETECTION METHOD FOR INFRARED IMAGES OF AERIAL PHOTOVOLTAIC MODULES BASED ON CSWIN[J]. Acta Energiae Solaris Sinica. 2023, 44(10): 142-147 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0846

References

[1] 高伟, 黄俊铭. 基于SSELM的光伏组件故障智能诊断方法[J]. 太阳能学报, 2021, 42(12): 465-470.
GAO W, HUANG J M.Intelligent fault diagnosis method of photovoltaic module via SSELM[J]. Acta energiae solaris sinica, 2021, 42(12): 465-470.
[2] 蒋琳, 苏建徽, 施永, 等. 基于红外热图像处理的光伏阵列热斑检测方法[J]. 太阳能学报, 2020, 41(8): 180-184.
JIANG L, SU J H, SHI Y, et al.Hot spots detection of operating PV arrays through IR thermal image[J]. Acta energiae solaris sinica, 2020, 41(8): 180-184.
[3] 蒋琳, 苏建徽, 李欣, 等. 基于可见光和红外热图像融合的光伏阵列热斑检测方法[J]. 太阳能学报, 2022, 43(1): 393-397.
JIANG L, SU J H, LI X, et al.Hot spot detection of photovoltaic array based on fusion of visible and infrared thermal images[J]. Acta energiae solaris sinica, 2022, 43(1): 393-397.
[4] SU B Y, CHEN H Y, LIU K, et al.RCAG-net: residual channelwise attention gate network for hot spot defect detection of photovoltaic farms[J]. IEEE transactions on instrumentation and measurement, 2021, 70: 1-14.
[5] 王道累, 李超, 李明山, 等. 基于深度卷积神经网络的光伏组件热斑检测[J]. 太阳能学报, 2022, 43(1): 412-417.
WANG D L, LI C, LI M S, et al.Solar photovoltaic modules hot spot detection based on deep convolutional neural networks[J]. Acta energiae solaris sinica, 2022, 43(1): 412-417.
[6] LIU Z, LIN Y T, CAO Y, et al.Swin transformer: hierarchical vision transformer using shifted windows[C]//2021 IEEE/CVF International Conference on Computer Vision (ICCV). Montreal, QC, Canada, 2022: 9992-10002.
[7] 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.
[8] LIN T Y, GOYAL P, GIRSHICK R, et al.Focal loss for dense object detection[J]. IEEE transactions on pattern analysis and machine intelligence, 2020, 42(2): 318-327.
[9] GE Z, LIU S T, WANG F, et al. YOLOX: exceeding YOLO series in2021[EB/OL]. 2021: arXiv: 2107.08430. https://arxiv.org/abs/2107.08430.
[10] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection[EB/OL].2020: arXiv: 2004. 10934. https://arxiv.org/abs/2004.10934.
[11] HE K M, ZHANG X Y, REN S Q, et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[C]//European Conference on Computer Vision, Cham, Switzerland, Springer, 2014: 346-361.
[12] DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16x16 words: transformers for image recognition at scale[EB/OL].2020: arXiv: 2010.11929. https://arxiv.org/abs/2010.11929.
[13] HE K M, ZHANG X Y, REN S Q, et al.Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016: 770-778.
[14] LIU W, ANGUELOV D, ERHAN D, et al.SSD: single shot MultiBox detector[C]//European Conference on Computer Vision, Cham,Switzerland, Springer, 2016: 21-37.
[15] LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA, 2017: 936-944.
[16] REDMON J, FARHADI A. YOLOv3: an incremental improvement[EB/OL].2018: arXiv: 1804.02767. https://arxiv.org/abs/1804.02767.