TrackSafe: A comparative study of data-driven techniques for automated railway track fault detection using image datasets
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In this paper , three object detection models: YOLOv5, Faster RCNN, and EfficientDet are compared by testing a dataset of 31 images that contain three different railway track elements (clip, rail, and fishplate), both faulty and non-faulty.About:
This article is published in Engineering Applications of Artificial Intelligence.The article was published on 2023-10-01 and is currently open access. It has received 0 citations till now. The article focuses on the topics: Computer science & Track (disk drive).read more
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features.
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Object Detection With Deep Learning: A Review
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A Survey on Performance Metrics for Object-Detection Algorithms
TL;DR: This work explores and compares the plethora of metrics for the performance evaluation of object-detection algorithms and proposes a standard implementation that can be used as a benchmark among different datasets with minimum adaptation on the annotation files.
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A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit
TL;DR: This work provides an overview of the most relevant evaluation methods used in object detection competitions, highlighting their peculiarities, differences, and advantages, and provides a novel open-source toolkit supporting different annotation formats and 15 performance metrics, making it easy for researchers to evaluate the performance of their detection algorithms in most known datasets.
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Railway track fastener defect detection based on image processing and deep learning techniques: A comparative study
TL;DR: In this article, a novel fastener defect detection and identification method using Dense-SIFT features is proposed which can achieve a better performance than the methods available in the literature.