Lei Yang
ABSTRACT
Due to ever-increasing power equipments and the distances of power transmission lines, insulator inspection presents a valuable but challenging issue. As a common insulator defect, missing-cap defects affect the structural strength of power insulators and cause irreparable harm to power supply security. Therefore, insulator defect detection is a basic and critical task for power line inspection. Most detection methods are mainly based on machine learning algorithms. Shallow learning methods rely on handcrafted image features and are always aimed at specific scenarios or prior knowledge. The unbalanced data sets of insulators affect the detection performance of deep learning algorithms. To address the above problems regarding insulator defect detection, a novel detection algorithm based on a cascading deep architecture is proposed for unmanned aerial vehicle (UAV) inspection. Combined with the strong detection performance of deep architecture, an insulator location algorithm based on the improved YOLOV3 model is proposed to remove complex backgrounds such as ”region of interest (ROI) extraction”. On this basis, a novel semantic segmentation algorithm is proposed to realize defect segmentation for small missing-cap defects. Experiments show that the proposed algorithm can satisfactorily meet the precision and robustness requirements of power line inspection compared with other related detection models.
- Haipeng Chen, Zhentao He, Bowen Shi, and Tie Zhong. 2019. Research on Recognition Method of Electrical Components Based on YOLO V3. IEEE Access 7(2019), 157818–157829.Google Scholar
Cross Ref
- Jiaming Han, Zhong Yang, Hao Xu, Guoxiong Hu, Chi Zhang, Hongchen Li, Shangxiang Lai, and Huarong Zeng. 2020. Search Like an Eagle: A Cascaded Model for Insulator Missing Faults Detection in Aerial Images. Energies 13, 3 (2020), 713.Google ScholarCross Ref
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 37, 9(2015), 1904–1916.Google ScholarDigital Library
- Gaoqiang Kang, Shibin Gao, Long Yu, and Dongkai Zhang. 2018. Deep architecture for high-speed railway insulator surface defect detection: Denoising autoencoder with multitask learning. IEEE Transactions on Instrumentation and Measurement 68, 8(2018), 2679–2690.Google ScholarCross Ref
- Jonathan Long, Evan Shelhamer, and Trevor Darrell. 2014. Fully Convolutional Networks for Semantic Segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 3431–3440.Google Scholar
- Xiren Miao, Xinyu Liu, Jing Chen, Shengbin Zhuang, Jianwei Fan, and Hao Jiang. 2019. Insulator detection in aerial images for transmission line inspection using single shot multibox detector. IEEE Access 7(2019), 9945–9956.Google ScholarCross Ref
- Joseph Redmon. 2017. YOLO9000: better, faster, stronger. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 7263–7271.Google ScholarCross Ref
- Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 779–788.Google ScholarCross Ref
- Joseph Redmon and Ali Farhadi. 2018. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767(2018).Google Scholar
- Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In Proceedings of International Conference on Medical Image Computing and Computer-assisted Intervention (MICCAI). Springer, 234–241.Google ScholarCross Ref
- Diana Sadykova, Damira Pernebayeva, Mehdi Bagheri, and Alex James. 2019. IN-YOLO: Real-time detection of outdoor high voltage insulators using UAV imaging. IEEE Transactions on Power Delivery 35, 3 (2019), 1599–1601.Google ScholarCross Ref
- Carlos Sampedro, Javier Rodriguez-Vazquez, Alejandro Rodriguez-Ramos, Adrian Carrio, and Pascual Campoy. 2019. Deep Learning-Based System for Automatic Recognition and Diagnosis of Electrical Insulator Strings. IEEE Access 7(2019), 101283–101308.Google ScholarCross Ref
- Xian Tao, Dapeng Zhang, Zihao Wang, Xilong Liu, Hongyan Zhang, and De Xu. 2018. Detection of power line insulator defects using aerial images analyzed with convolutional neural networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems 50, 4(2018), 1486–1498.Google ScholarCross Ref
- Jiqing Wu, Zhiwu Huang, Janine Thoma, Dinesh Acharya, and Luc Van Gool. 2018. Wasserstein divergence for gans. In Proceedings of the European Conference on Computer Vision (ECCV). 653–668.Google ScholarDigital Library
- Junping Zhong, Zhigang Liu, Zhiwei Han, Ye Han, and Wenxuan Zhang. 2018. A CNN-based defect inspection method for catenary split pins in high-speed railway. IEEE Transactions on Instrumentation and Measurement 68, 8(2018), 2849–2860.Google ScholarCross Ref
Index Terms
- An Intelligent Fault Location Algorithm of High Voltage Lines Using Cascading Deep Network
Recommendations
A power line segmentation model in aerial images based on an efficient multibranch concatenation network
AbstractInspection of a power transmission line by an unmanned aerial vehicle (UAV) is an important measure to ensure the operational safety of the power grid. A semantic segmentation model based on deep learning can assist UAVs in extracting power lines,...
A light defect detection algorithm of power insulators from aerial images for power inspection
AbstractWith the rapid growth of high-voltage transmission lines, the number of power transmission line equipments is correspondingly increasing. Power insulator is the basic component which plays the key role in the stable operation of power system. As a ...
Threshold voltage degradation under high Vgs and low Vds on 200V SOI power devices
The hot-carrier degradation behavior of the 200V lateral insulated gate bipolar transistor and lateral diffused MOS transistor both on SOI substrates (SOI-LIGBT and SOI-LDMOS) under high Vgs and low Vds is experimentally investigated. It is shown that ...
Comments