Automatic Defect Detection of Fasteners on the Catenary Support Device Using Deep Convolutional Neural Network

By Junwen Chen, Zhigang Liu, Hongrui Wang, Alfredo Núñez, Zhiwei Han. IEEE Transactions on Instrumentation and Measurement, 2018

383 citations recorded by the source corpus.

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Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks (2018, background): “[35] proposed a detection system that has three deep convolutional neural network (DCNN) based detection stages, including two detectors to localize key components and a classifier to diagnose their status.”
A Review on State-of-the-Art Power Line Inspection Techniques (2020, background): “as inspection of power line components [1]–[4], inspection of power lines [5], inspection of power towers [6], and inspection of power line defects [7]–[9].”
A review on modern defect detection models using DCNNs – Deep convolutional neural networks (2021, methodology): “In [45] a method for detecting defects of fasteners on the cate-nary support device (for electrical trains) using a deep CNN is presented.”
Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges (2020, background): “Several factors that influence the precision of Magnetic powder testing include roughness and the profile of the test piece, the geometrical characteristics of defects, the selected magnetization method, and the quality of operators [42].”
Detection of Power Line Insulator Defects Using Aerial Images Analyzed With Convolutional Neural Networks (2020, methodology): “The precision and recall obtained by the method in [27] are close to ours, and a cascaded object detection framework was also used.”