基于YOLOv4-MP的绝缘子爆裂缺陷检测方法

doi:10.12052/gdutxb.220079

Abstract

Abstract: Aiming at the problems of small defect targets and complex backgrounds, which leads to low accuracy in insulator burst defect detection, an improved detection algorithm YOLOv4-MP based on YOLOv4 is proposed. First, the Shuffle Attention module is included in the feature extraction network to limit the interference of complicated background, enabling the model to extract more effective feature information. Then, the spatial pyramid pooling is enhanced by the dilated pooling layer, which effectively increases the receptive field and improves the effect of feature fusion. Finally, the Mish function is utilized as the activation function of the path enhancement network to limit the loss of low-level information. The experimental results show that the mean average precision of YOLOv4-MP reaches 93.60%, which is 6.37% higher than that of the YOLOv4 algorithm. Compared with the commonly used detection algorithms, YOLOv4-MP has better detection performance and has high application value for the detection of insulator burst defects.

Key words: object detection, insulator detection, convolutional neural network, feature fusion, transmission line inspection

CLC Number:

TP391.4

Xie Guo-bo, Lin Li, Lin Zhi-yi, He Di-xuan, Wen Gang. An Insulator Burst Defect Detection Method Based on YOLOv4-MP[J].Journal of Guangdong University of Technology, 2023, 40(02): 15-21.

References

[1] SANYAL S, ASLAM F, KIM T, et al. Deterioration of porcelain insulators utilized in overhead transmission lines: a review [J]. Transactions on Electrical and Electronic Materials, 2020, 21(1): 16-21.
[2] MIAO X, LIU X, CHEN J, et al. Insulator detection in aerial images for transmission line inspection using single shot multibox detector [J]. IEEE Access, 2019, 7: 9945-9956.
[3] CHEN M H, TIAN Y N, XING S Y, et al. Environment perception technologies for power transmission line inspection robots [J]. Journal of Sensors, 2021, 2021(2): 1-16.
[4] 杨罡, 孙昌雯, 王大伟, 等. 基于无人机前端和SSD算法的输电线路部件检测模型对比研究[J]. 太原理工大学学报, 2020, 51(2): 212-219.
YANG G, SUN C W, WANG D W, et al. Comparative study of transmission line component detection models based on UAV front end and SSD algorithm [J]. Journal of Taiyuan University of Technology, 2020, 51(2): 212-219.
[5] LIU X, MIAO X, JIANG H, et al. Data analysis in visual power line inspection: an in-depth review of deep learning for component detection and fault diagnosis [J]. Annual Reviews in Control, 2020, 50: 253-277.
[6] TAN P, LI X F, XU J M, et al. Catenary insulator defect detection based on contour features and gray similarity matching [J]. Journal of Zhejiang University - Science A:Applied Physics & Engineering, 2020, 21(1): 64-73.
[7] 闫丽梅, 刘永强, 徐建军, 等. 基于Grabcut分割和填充物面积判别的复合绝缘子断串诊断[J]. 电力系统保护与控制, 2021, 49(22): 114-119.
YAN L M, LIU Y Q, XU J J, et al. Broken string diagnosis of composite insulator based on Grabcut segmentation and filler area discrimination [J]. Power System Protection and Control, 2021, 49(22): 114-119.
[8] ZHAI Y, CHEN R, YANG Q, et al. Insulator fault detection based on spatial morphological features of aerial images [J]. IEEE Access, 2018, 6: 35316-35326.
[9] 王银立, 闫斌. 基于视觉的绝缘子“掉串”缺陷的检测与定位[J]. 计算机工程与设计, 2014, 35(2): 583-587.
WANG Y L, YAN B. Vision based detection and location for cracked insulator [J]. Computer Engineering and Design, 2014, 35(2): 583-587.
[10] 姜云土, 韩军, 丁建, 等. 基于多特征融合的玻璃绝缘子识别及自爆缺陷的诊断[J]. 中国电力, 2017, 50(5): 52-58.
JIANG Y T, HAN J, DING J, et al. The identification and diagnosis of self-blast defects of glass insulators based on multi-feature fusion [J]. Electric Power, 2017, 50(5): 52-58.
[11] ZHAI Y, WANG D, ZHANG M, et al. Fault detection of insulator based on saliency and adaptive morphology [J]. Multimedia Tools and Applications, 2017, 76(9): 12051-12064.
[12] 黄剑航, 王振友. 基于特征融合的深度学习目标检测算法研究[J]. 广东工业大学学报, 2021, 38(4): 52-58.
HUANG J H, WANG Z Y. A Research on deep learning object detection algorithm based on feature fusion [J]. Journal of Guangdong University of Technology, 2021, 38(4): 52-58.
[13] ZHAO W, XU M, CHENG X, et al. An insulator in transmission lines recognition and fault detection model based on improved faster RCNN [J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-8.
[14] 李鑫, 刘帅男, 杨桢, 等. 基于改进Cascade R-CNN的输电线路多目标检测[J]. 电子测量与仪器学报, 2021, 35(10): 24-32.
LI X, LIU S N, YANG Z, et al. Multi-target detection of transmission lines based on improved cascade R-CNN [J]. Journal of Electronic Measurement and Instrumentation, 2021, 35(10): 24-32.
[15] 赵文清, 张海明, 徐敏夫. 面向改进尺度缩放网络的绝缘子识别[J]. 中国图象图形学报, 2021, 26(11) : 2561-2570.
ZHAO W Q, ZHANG H M, XU M F. Insulator recognition based on an improved scale-transferrable network[J] Journal of Image and Graphics, 2021, 26(11) : 2561-2570.
[16] LIU J, LIU C, WU Y, et al. An improved method based on deep learning for insulator fault detection in diverse aerial images [J]. Energies, 2021, 14(14): 4365.
[17] ZHENG R, ZHU L, HU T, et al. Detection of fault insulator of power transmission line based on region-CNN[C]//2020 35th Youth Academic Annual Conference of Chinese Association of Automation (YAC) . Zhanjiang: IEEE, 2020: 73-76.
[18] LIAO G P, YANG G J, TONG W T, et al. Study on power line insulator defect detection via improved faster region-based convolutional neural network[C]//2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT) . Dalian: IEEE, 2019: 262-266.
[19] 唐小煜, 黄进波, 冯洁文, 等. 基于 U-net 和 YOLOv4 的绝缘子图像分割与缺陷检测[J]. 华南师范大学学报 (自然科学版) , 2020, 52(6): 15-21.
TANG X Y, HUANG J B, FENG J W, et al. Image segmentation and defect detection of insulators based on U-net and YOLOv4 [J]. Journal of South China Normal University (Natural Science Edition) , 2020, 52(6): 15-21.
[20] 党宏社, 薛萌, 郭琴. 基于改进的YOLOv4绝缘子掉片故障检测方法[J]. 电瓷避雷器, 2022, 65(1): 211-218.
DANG H S, XUE M, GUO Q. Insulator dropout fault detection method based on improved YOLOv4 [J]. Insulators and Surge Arrester, 2022, 65(1): 211-218.
[21] ZHANG Q L, YANG Y B. Sa-net: Shuffle attention for deep convolutional neural networks[C]//ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . [S. l. ]: IEEE, 2021: 2235-2239.
[22] MA N, ZHANG X, ZHENG H T, et al. Shufflenet v2: Practical guidelines for efficient cnn architecture design[C]//Proceedings of the European Conference on Computer Vision (ECCV) . Munich: [s. n. ], 2018: 116-131.
[23] CHEN L C, PAPANDREOU G, KOKKINOS I, et al. Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 40(4): 834-848.

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An Insulator Burst Defect Detection Method Based on YOLOv4-MP

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