A Cascaded Insulator Defect Detection Model Combining Semantic Segmentation and Object Detection

doi:10.3969/j.issn.1006-2475.2023.06.014

Abstract

Abstract: Insulator defect detection is an important part of the routine inspection of substations. Using the video surveillance images in the substation, we propose a cascaded insulator defect detection model that combines semantic segmentation with object detection aiming at the low detection accuracy of the insulator defect detection model. The model consists of three modules： Insulator segmentation, insulator cutting and defect detection. The insulator segmentation module separates the insulator from the complex environment and proposes an edge enhancement loss function. The insulator cutting module uses the image processing method to obtain the insulator region aligned with the axis. The defect detection module completes the defect detection. Experiment results show that the accuracy of edge-enhanced Unet for insulator segmentation reaches 83.99%, and the accuracy of RetinaNet with improved anchor generation method for defect detection reaches 63.32%. Compared with the single-stage insulator defect detection model, the proposed cascade insulator defect detection model can effectively eliminate the interference of environment, detecting most of the insulator defects.

Key words: cascaded insulator defect detection model, image processing, semantic segmentation, object detection

CLC Number:

TP309

YE Li-ming, CHEN Wei-wen. A Cascaded Insulator Defect Detection Model Combining Semantic Segmentation and Object Detection[J]. Computer and Modernization, 2023, 0(06): 82-88.

References 27

[1]	张晶晶,韩军,赵亚博,等. 形状感知的绝缘子识别与缺陷诊断[J]. 中国图象图形学报, 2014,19(8):1194-1201.
[2]	姜浩然,金立军,闫书佳. 航拍图像中绝缘子的识别与故障诊断[J]. 机电工程, 2015,32(2):274-278.
[3]	ZHAI Y J, WANG D, ZHANG M L, et al.Fault detection of insulator based on saliency and adaptive morphology[J]. Multimedia Tools and Applications, 2017,76(9):12051-12064.
[4]	CHENG H, ZHAI Y, CHEN R, et al.Self-shattering defect detection of glass insulators based on spatial features[J]. Energies, 2019,12(3):543-547.
[5]	周莉莉,姜枫. 图像分割方法综述研究[J]. 计算机应用研究, 2017,34(7):1921-1928.
[6]	LI S, ZHOU H, WANG G, et al.Cracked insulator detection based on R-FCN[J]. Journal of Physics Conference Series, 2018,1069(1). DOI:10.1088/1742-6596/1069/1/012147.
[7]	DAI J F, LI Y, HE K M, et al.R-FCN: Object detection via region-based fully convolutional networks[C]// Proceedings of the 30th International Conference on Neural Information Processing Systems. 2016:379-387.
[8]	SHRIVASTAVA A, GUPTA A, GIRSHICK R.Training region-based object detectors with online hard example mining[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016:761-769.
[9]	RONNEBERGER O, FISCHER P, BROX T. U-Net: Convolutional networks for biomedical image segmentation[J]. arxiv preprint arxiv:1505.04597,2015.
[10]	田萱,王子亚,王建新.基于语义分割的食品标签文本检测[J].农业机械学报,2020,51(08):336-343.
[11]	司垒,王忠宾,熊祥祥,等. 基于改进U-net网络模型的综采工作面煤岩识别方法[J]. 煤炭学报, 2021,46(S1):578-589.
[12]	LONG J, SHELHAMER E, DARRELL T.Fully convolutional networks for semantic segmentation[C]// 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2015:3431-3440.
[13]	ZHANG J, LIN S, DING L, et al.Multi-scale context aggregation for semantic segmentation of remote sensing images[J]. Remote Sensing, 2020,12(4):701.
[14]	ASGARI T S, ABHISHEK K, COHEN J P, et al.Deep semantic segmentation of natural and medical images: A review[J]. Artificial Intelligence Review, 2021,54(1):137-178.
[15]	CANNY J.A computational approach to edge detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986(6):679-698.
[16]	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.
[17]	LIU W, ANGUELOV D, ERHAN D, et al.SSD: Single shot multiBox detector[C]// Computer Vision - ECCV 2016. 2016:21-37.
[18]	REDMON J, DIVVALA S, GIRSHICK R, et al.You only look once: Unified, real-time object detection[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016:779-788.
[19]	LIN T Y, GOYAL P, GIRSHICK R, et al.Focal loss for dense object detection[C]// 2017 IEEE International Conference on Computer Vision (ICCV). 2017:2999-3007.
[20]	HAN J M, YANG Z, ZHANG Q Y, et al.A method of insulator faults detection in aerial images for high-voltage transmission lines inspection[J]. Applied Sciences, 2019,9(10):2009.
[21]	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). 2016:770-778.
[22]	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). 2017:936-944.
[23]	MACQUEEN J.Some methods for classification and analysis of multivariate observations[C]// Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability. 1967,1(14):281-297.
[24]	OTSU N.A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979,9(1):62-66.
[25]	张莉,贾永红,程刚. 基于数学形态学的遥感影像边缘检测研究[J]. 地理空间信息, 2006(4):52-54.
[26]	REDMON J, FARHADI A.YOLO9000: Better, faster, stronger[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017:6517-6525.
[27]	REDMON J, FARHADI A. YOLOv3: An incremental improvement[J]. arXiv preprint arXiv:1804.02767, 2018.