RESEARCH ON PERIMETER INTRUSION DETECTION OF PHOTOVOLTAIC POWER STATION BASED ON BIMODAL FUSION

Hou Beiping; Chen Jiahao; Zhu Yuzhen; Zheng Yangbin; Yu Aihua

doi:10.19912/j.0254-0096.tynxb.2023-1118

PDF(2983 KB)

Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (11) : 289-295. DOI: 10.19912/j.0254-0096.tynxb.2023-1118

RESEARCH ON PERIMETER INTRUSION DETECTION OF PHOTOVOLTAIC POWER STATION BASED ON BIMODAL FUSION

Hou Beiping^1,2, Chen Jiahao¹, Zhu Yuzhen¹, Zheng Yangbin¹, Yu Aihua^1,2

Author information +

History +

Abstract

An improved perimeter intrusion detection algorithm （visual thermal detection transformer, VT-DETR） is proposed based on bimodal feature fusion to solve the problems of insufficient perimeter intrusion detection data, low accuracy and incomplete detection of foreign objects in distributed photovoltaic power plants. Initially, the single-channel network is expanded into a dual-channel network, enabling simultaneous extraction of multidimensional features from visible light and infrared images. Subsequently, a strategy for soft weight allocation is introduced to compensate for the absence of thermal radiation information during the fusion of visible light and infrared images. Finally, the loss function calculation method is enhanced to improve the training efficiency of the attention mechanism. Additionally, a dedicated perimeter intrusion image dataset is built for the context of distributed photovoltaic power plants. Experimental results show the efficiency of the enhanced algorithm, achieving an average detection accuracy of 92.08% and thus validating its effectiveness to perimeter intrusion detection in distributed photovoltaic power plants.

Key words

deep learning / object detection / image fusion / distributed photovoltaic power station / perimeter intrusion

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Hou Beiping, Chen Jiahao, Zhu Yuzhen, Zheng Yangbin, Yu Aihua. RESEARCH ON PERIMETER INTRUSION DETECTION OF PHOTOVOLTAIC POWER STATION BASED ON BIMODAL FUSION[J]. Acta Energiae Solaris Sinica. 2024, 45(11): 289-295 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1118

References

[1] 蒋兴群, 刘波, 宋力, 等. 基于改进YOLO-v3的风力机叶片表面损伤检测识别[J]. 太阳能学报, 2023, 44(3): 212-217.
JIANG X Q, LIU B, SONG L, et al.Surface damage detection and recognition of wind turbine blade based on improved YOLO-v3[J]. Acta energiae solaris sinica, 2023, 44(3): 212-217.
[2] 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.
[3] 郭保青, 杨柳旭, 史红梅, 等. 基于快速背景差分的高速铁路异物侵入检测算法[J]. 仪器仪表学报, 2016, 37(6): 1371-1378.
GUO B Q, YANG L X, SHI H M, et al.High-speed railway clearance intrusion detection algorithm with fast background subtraction[J]. Chinese journal of scientific instrument, 2016, 37(6): 1371-1378.
[4] 管岭, 贾利民, 谢征宇. 融合注意力机制的轨道入侵异物检测轻量级模型研究[J]. 铁道学报, 2023, 45(5): 72-81.
GUAN L, JIA L M, XIE Z Y.Research on lightweight model for railway intrusion detection integrating attention mechanism[J]. Journal of the China Railway Society, 2023, 45(5): 72-81.
[5] 王瑞峰, 陈小屹. 基于改进YOLOv5的轨道异物入侵检测算法研究[J]. 云南大学学报(自然科学版), 2023, 45(4): 799-806.
WANG R F, CHEN X Y.Research on orbital foreign object intrusion detection algorithm based on improved YOLOv5[J]. Journal of Yunnan University (natural sciences edition), 2023, 45(4): 799-806.
[6] 肖曾翔, 徐启峰. 基于改进卷积神经网络的变电站异物入侵识别[J]. 科学技术与工程, 2022, 22(4): 1465-1471.
XIAO Z X, XU Q F.Recognition of foreign objects intrusion in substation based on improved convolutional neural network[J]. Science technology and engineering, 2022, 22(4): 1465-1471.
[7] 宁欣, 田伟娟, 于丽娜, 等. 面向小目标和遮挡目标检测的脑启发CIRA-DETR全推理方法[J]. 计算机学报, 2022, 45(10): 2080-2092.
NING X, TIAN W J, YU L N, et al.Brain-inspired CIRA-DETR full inference model for small and occluded object detection[J]. Chinese journal of computers, 2022, 45(10): 2080-2092.
[8] YANG L, MA R, ZAKHOR A. Drone object detection using rgb/ir fusion[J]. arXiv preprint arXiv :2201.03786, 2022.
[9] DEVAGUPTAPU C, AKOLEKAR N, SHARMA M M, et al.Borrow from anywhere: pseudo multi-modal object detection in thermal imagery[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Long Beach, CA, USA, 2019: 1029-1038.
[10] GAO C Q, MENG D Y, YANG Y, et al.Infrared patch-image model for small target detection in a single image[J]. IEEE transactions on image processing, 2013, 22(12): 4996-5009.
[11] LI Y M, ZHANG W, LIU Y Y, et al.An efficient fire and smoke detection algorithm based on an end-to-end structured network[J]. Engineering applications of artificial intelligence, 2022, 116: 105492.
[12] CARION N, MASSA F, SYNNAEVE G, et al.End-to-end object detection with transformers[M]. Cham: Springer International Publishing, 2020: 213-229.
[13] 徐硕, 邓艾东, 杨宏强, 等. 基于改进残差网络的旋转机械故障诊断[J]. 太阳能学报, 2023, 44(7): 409-418.
XU S, DENG A D, YANG H Q, et al.Rotating machinery fault diagnosis method based on improved residual neural network[J]. Acta energiae solaris sinica, 2023, 44(7): 409-418.
[14] LEYKIN A, RAN Y, HAMMOUD R.Thermal-visible video fusion for moving target tracking and pedestrian classification[C]//2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA, 2007: 1-8.
[15] 赵明, 张浩然. 一种基于跨域融合网络的红外目标检测方法[J]. 光子学报, 2021, 50(11): 339-349.
ZHAO M, ZHANG H R.An infrared object detection method based on cross-domain fusion network[J]. Acta photonica sinica, 2021, 50(11): 339-349.
[16] SHAW P, USZKOREIT J, VASWANI A. Self-attention with relative position representations[J]. arXiv preprint arXiv:1803. 02155,2018.
[17] DAI Y N, YU J Y, ZHANG D A, et al.RODFormer: high-precision design for rotating object detection with transformers[J]. Sensors, 2022, 22(7): 2633.