%0 Journal Article
%T 轮轨踏面损伤检测算法综述<br>Research on Track Wear Using Object Detection Technology
%A 李知宇
%A 李士心
%J Computer Science and Application
%P 1465-1472
%@ 2161-881X
%D 2023
%I Hans Publishing
%R 10.12677/CSA.2023.137145
%X <span style=\"font-size:14px;\">随着人工智能技术的迅猛发展，面对种类繁多的轮轨踏面损伤，深度学习提供了一种高效、准确、自动化的检测算法，有助于及时发现和处理踏面损伤问题，提升铁路运行的安全性和可靠性。本文将详细阐述了Canny边缘检测算法、Faster R-CNN算法、YOLO算法和SSD算法等四种算法的基本原理、研究现状及其存在的不足，并对进一步研究进行了展望。</span><br />
<span style=\"font-size:14px;\">With the rapid development of artificial intelligence technology, in the face of a wide variety of wheel-rail tread damage, deep learning provides an efficient, accurate and automated detection algorithm, which is helpful to timely discover and deal with tread damage problems, and improve the safety and reliability of railway operation. In this paper, the basic principles, research status and shortcomings of Canny edge detection algorithm, Faster R-CNN algorithm, YOLO algorithm and SSD algorithm are described in detail, and further research is prospected.</span>
%K 目标检测，踏面损伤，YOLO算法，卷积神经网络，Canny算法，SSD算法<br>Object Detection
%K Tread Injury
%K YOLO Algorithm
%K Convolutional Neural Networks
%K Canny Algorithm
%K SSD Algorithm
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=69635