文章基本信息

标题：An in-depth assessment of convolutional neural networks for rail surface defect detection
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作者：Rebeca Alves da Silva Lemos Passos ; Matheus Pinheiro Ferreira ; Ben-Hur de Albuquerque e Silva 等
期刊名称：Research, Society and Development
电子版ISSN：2525-3409
出版年度：2022
卷号：11
期号：8
页码：1-13
DOI：10.33448/rsd-v11i8.30252
语种：English
出版社：Grupo de Pesquisa Metodologias em Ensino e Aprendizagem em Ciências
摘要：The consistent monitoring of rails is based on correctly identifying defects to support corrective measures. Recently, convolutional neural networks (CNN), a deep learning method, have been providing outstanding results for the automatic detection of defects. However, several aspects of CNN-based approaches such as network architecture, transfer learning and processing time remains not fully understood. In this work, we performed an in-depth assessment of ten widely used CNN models with the objective of finding the one with the best performance in identifying defects in rail surface images. The classification results are promising, reaching an average accuracy of 83.7% on detection of mild defects and squat. The Inceptionv3 network provided the best results by correctly identifying 92% of images with severe squat defects.
关键词：Rail inspection;Squat;CNN.