Detection Method Based on Automatic Visual Shape Clustering for Pin-Missing Defect in Transmission Lines

Bolts are the most numerous fasteners in transmission lines and are prone to losing their split pins. How to realize the automatic pin-missing defect detection for bolts in transmission lines so as to achieve timely and efficient troubleshooting is a difficult problem and the long-term research target of power systems. In this article, an automatic detection model called automatic visual shape clustering network (AVSCNet) for the pin-missing defect is constructed. First, an unsupervised clustering method for the visual shapes of bolts is proposed and applied to construct a defect detection model that can learn the difference in visual shape. Next, three deep convolutional neural network optimization methods are used in the model: the feature enhancement, feature fusion, and region feature extraction. The defect detection results are obtained by applying the regression calculation and classification to the regional features. In this article, the object detection model of different networks is used to test the data set of pin-missing defect constructed by the aerial images of transmission lines from multiple locations, and it is evaluated by various indicators and is fully verified. The results show that our method can achieve considerably satisfactory detection effect.

[1]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Robert Jenssen,et al.  Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning , 2018, International Journal of Electrical Power & Energy Systems.

[3]  Peng Yuan,et al.  The Application of One-Class Classifier Based on CNN in Image Defect Detection , 2017 .

[4]  Zhigang Liu,et al.  Automatic Defect Detection of Fasteners on the Catenary Support Device Using Deep Convolutional Neural Network , 2018, IEEE Transactions on Instrumentation and Measurement.

[5]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[7]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[9]  Kai Liu,et al.  Location and fault detection of catenary support components based on deep learning , 2018, 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).

[10]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[11]  Long Chen,et al.  Automatic Fastener Classification and Defect Detection in Vision-Based Railway Inspection Systems , 2014, IEEE Transactions on Instrumentation and Measurement.

[12]  Kai Han,et al.  A new method in wheel hub surface defect detection: Object detection algorithm based on deep learning , 2017, 2017 International Conference on Advanced Mechatronic Systems (ICAMechS).

[13]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[15]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Ye Han,et al.  A CNN-Based Defect Inspection Method for Catenary Split Pins in High-Speed Railway , 2019, IEEE Transactions on Instrumentation and Measurement.

[17]  Roland Siegwart,et al.  BRISK: Binary Robust invariant scalable keypoints , 2011, 2011 International Conference on Computer Vision.

[18]  Kaiming He,et al.  Mask R-CNN , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[19]  Reinhold Huber-Mörk,et al.  Convolutional Neural Networks for Steel Surface Defect Detection from Photometric Stereo Images , 2014, ISVC.

[20]  Wei Zhang,et al.  Unified Vision‐Based Methodology for Simultaneous Concrete Defect Detection and Geolocalization , 2018, Comput. Aided Civ. Infrastructure Eng..

[21]  Tan Min,et al.  Mobile Robot for Overhead Powerline Inspection-a Review , 2004 .

[22]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Gabriel Thomas,et al.  Detection of Ice on Power Cables Based on Image Texture Features , 2018, IEEE Transactions on Instrumentation and Measurement.

[24]  Junyong Liu,et al.  Unmanned Aerial Vehicles for Power Line Inspection: A Cooperative Way in Platforms and Communications , 2014, J. Commun..

[25]  Shaharyar Ahmed Khan Tareen,et al.  A comparative analysis of SIFT, SURF, KAZE, AKAZE, ORB, and BRISK , 2018, 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET).

[26]  Hua Yang,et al.  An Unsupervised-Learning-Based Approach for Automated Defect Inspection on Textured Surfaces , 2018, IEEE Transactions on Instrumentation and Measurement.

[27]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[28]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[29]  Yi Fang,et al.  UAV Low Altitude Photogrammetry for Power Line Inspection , 2016, ISPRS Int. J. Geo Inf..

[30]  Janos Toth,et al.  Smart view for a smart grid — Unmanned Aerial Vehicles for transmission lines , 2010, 2010 1st International Conference on Applied Robotics for the Power Industry.

[31]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[32]  Li Baoshu,et al.  Application of Image Processing in Patrol Inspection of Overhead Transmission Line by Helicopter , 2010 .

[33]  Ehab Salahat,et al.  Recent advances in features extraction and description algorithms: A comprehensive survey , 2017, 2017 IEEE International Conference on Industrial Technology (ICIT).

[34]  Matti Pietikäinen,et al.  Deep Learning for Generic Object Detection: A Survey , 2018, International Journal of Computer Vision.

[35]  Joseph Redmon,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[36]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.