Rapid Detection of Cold Heavy Rail Surface Defects of Production Line Based on One-Dimensional Convolution Network

doi:10.12068/j.issn.1005-3026.2021.02.018

Abstract

Abstract: An intuitive and efficient method based on machine vision was applied to the automatic detection of cold heavy rail surface defects of production line. Color binocular linear scan camera gathered the depth information and RGB information. Depth information was employed for the rapid detection of defects， and for defect segmentation combined with RGB information. Then a deep learning network was proposed for the rapid detection of defects. The network mainly includes a feature extractor based on one-dimensional convolution network， a classifier composed of full connection layers and dropout layers， and a filter with size prior. Finally， a data acquisition platform was setup and the data of heavy rail samples were collected for the verification of network performance. The results show that the network proposed performs well. The defect-level detection rate is 100% and the false detection rate is 35% on the collected data， which is better than that of the compared networks.

Key words: cold heavy rail of production line; surface defect; machine vision; depth information; one-dimensional convolution network

CLC Number:

TP391.4

ZHANG De-fu， SONG Ke-chen， NIU Meng-hui， YAN Yun-hui. Rapid Detection of Cold Heavy Rail Surface Defects of Production Line Based on One-Dimensional Convolution Network[J]. Journal of Northeastern University(Natural Science), 2021, 42(2): 276-281.

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