PHOTOVOLTAIC HOT SPOT RECOGNITION BASED ON FEATURE PYRAMID FUSION HIGH RESOLUTION NETWORK

Sun Hairong; Li Li; Zhou Yingjie; Zhou Lihui

doi:10.19912/j.0254-0096.tynxb.2022-0684

PDF(1959 KB)

Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (9) : 109-116. DOI: 10.19912/j.0254-0096.tynxb.2022-0684

PHOTOVOLTAIC HOT SPOT RECOGNITION BASED ON FEATURE PYRAMID FUSION HIGH RESOLUTION NETWORK

Sun Hairong¹, Li Li¹, Zhou Yingjie¹, Zhou Lihui²

Author information +

History +

Abstract

Aiming at the problems existing in the photovoltaic hot spot recognition algorithm, such as the complex calculation of deep network parameters, the easy disappearance of gradient information and the reduced accuracy of model degradation, a photovoltaic hot spot identification and detection algorithm based on feature pyramid fusion high-resolution network is proposed. Firstly, a network model with parallel connection of multi-resolution subnetworks is build in this algorithm, which solves the problems of loss of detailed information and redundant features of hot spots in deep networks. Secondly, the multi-scale fusion module of the feature pyramid is introduced, which connects the feature maps of different scales across layers, solves the feature semantic gap, and improves the accuracy of model recognition. The experimental results show that the classification effect of the proposed algorithm on the photovoltaic infrared hot spot image dataset is better than the classical deep convolutional neural network algorithm, and the accuracy rate can reach 97.2%. The algorithm realizes high-precision and high-resolution hot spot detection and identification.

Key words

photovoltaic effect / feature extraction / image classification / high-resolution network / hot spot

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Sun Hairong, Li Li, Zhou Yingjie, Zhou Lihui. PHOTOVOLTAIC HOT SPOT RECOGNITION BASED ON FEATURE PYRAMID FUSION HIGH RESOLUTION NETWORK[J]. Acta Energiae Solaris Sinica. 2023, 44(9): 109-116 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0684

References

[1] 唐圣学, 邢玥, 陈丽, 等. 太阳电池组串热斑防治策略研究[J]. 太阳能学报, 2022, 43(4): 226-235.
TANG S X, XING Y, CHEN L, et al.Study on suppressing strategy of hot spot in solar cell series[J]. Acta energiae solaris sinica, 2022, 43(4): 226-235.
[2] 蔡洁聪, 吕洪坤, 朱凌云, 等. 光伏发电站热斑检测技术综述[J]. 电源技术, 2021, 45(5): 683-685.
CAI J C, LYU H K, ZHU L Y, et al.Review of hot spot detection technology in photovoltaic power station[J]. Chinese journal of power sources, 2021, 45(5): 683-685.
[3] 张珂, 冯晓晗, 郭玉荣, 等. 图像分类的深度卷积神经网络模型综述[J]. 中国图象图形学报, 2021, 26(10): 2305-2325.
ZHANG K, FENG X H, GUO Y R, et al.Overview of deep convolutional neural networks for image classification[J]. Journal of image and graphics, 2021, 26(10): 2305-2325.
[4] 盖荣丽, 蔡建荣, 王诗宇, 等. 卷积神经网络在图像识别中的应用研究综述[J]. 小型微型计算机系统, 2021, 42(9): 1980-1984.
GAI R L,CAI J R, WANG S Y, et al.Research review on image recognition based on deep learning[J]. Journal of Chinese computer systems , 2021, 42(9): 1980-1984.
[5] 王宪保, 李洁, 姚明海, 等. 基于深度学习的太阳能电池片表面缺陷检测方法[J]. 模式识别与人工智能, 2014, 27(6): 517-523.
WANG X B, LI J, YAO M H, et al.Solar cells surface defects detection based on deep learning[J]. Pattern recognition and artificial intelligence, 2014, 27(6): 517-523.
[6] 周颖, 毛立, 张燕, 等. 改进CNN的太阳电池缺陷识别方法研究[J]. 太阳能学报, 2020, 41(12): 69-76.
ZHOU Y, MAO L, ZHANG Y,et al.Research on defect detection and classification for solar cells based on improved convolutional neural network[J]. Acta energiae solaris sinica, 2020, 41(12): 69-76.
[7] 刘云鹏, 张喆, 裴少通, 等. 基于深度学习的红外图像中劣化绝缘子片的分割方法[J]. 电测与仪表, 2022, 59(9): 63-68, 118.
LIU Y P, ZHANG Z, PEI S T, et al.Faulty insulator segmentation method in infrared image based on deep learning[J]. Electrical measurement & instrumentation, 2022, 59(9): 63-68, 118.
[8] SUN K, XIAO B, LIU D, et al.Deep high-resolution representation learning for human pose estimation[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, CA, USA, 2019.
[9] 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.
[10] 陈科圻, 朱志亮, 邓小明, 等. 多尺度目标检测的深度学习研究综述[J]. 软件学报, 2021, 32(4): 1201-1227.
CHEN K Q, ZHU Z L, DENG X M, et al.Deep learning for multi-scale object detection: a survey[J]. Journal of software, 2021, 32(4): 1201-1227.
[11] LIN T Y, DOLLAR 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.
[12] 张雪松, 庄严, 闫飞, 等. 基于迁移学习的类别级物体识别与检测研究与进展[J]. 自动化学报, 2019, 45(7): 1224-1243.
ZHANG X S, ZHUANG Y, YAN F, et al.Status and development of transfer learning based category-level object recognition and detection[J]. Acta automatica sinica, 2019, 45(7): 1224-1243.
[13] 侯丽. 基于AdaBoost和深度学习的红外乳腺癌检测方法研究[D]. 武汉: 华中科技大学, 2017.
HOU L.Detection of breast cancer with infrared thermal images based on AdaBoost and deep learning[D]. Wuhan: Huazhong University of Science and Technology, 2017.