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Feature Recognition and Detection for Common Maintenance Tools Based on Deep Learning

[+] Author Affiliations
Chengcheng Liu, Kuan Zhang, Shengyuan Li, Xuefeng Zhao

Dalian University of Technology, Dalian, China

Peigang Li

Shanghai Institute of Technology, Shanghai, China

Paper No. SMASIS2018-8266, pp. V002T05A016; 6 pages
  • ASME 2018 Conference on Smart Materials, Adaptive Structures and Intelligent Systems
  • Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting; Emerging Technologies
  • San Antonio, Texas, USA, September 10–12, 2018
  • Conference Sponsors: Aerospace Division
  • ISBN: 978-0-7918-5195-1
  • Copyright © 2018 by ASME


With the rapid development of rail traffic, the importance of railway overhaul is becoming increasingly prominent. Making an inventory on tools is an important step that railway workers must take before and after railway inspection. The tools left on the railway will cause great harm to train safety. To avoid this happening, the commonly used method is manual inventory at present, which is time-consuming, laborious and easily leads to omissions. In order to overcome these shortcomings, this paper proposes a Faster Region-based Convolutional Neural Network (Faster R-CNN)-based method for tools inventory. To realize the method, a Faster R-CNN architecture based on ZF-Net is modified and a database including a large number of images for 10 types of tools is built. Then the Faster R-CNN is trained and validated using the built database. The performance of the trained Faster R-CNN is evaluated using some new images which are not be used for training process. The result shows 95.0325% average precision (AP) ratings for 10 different types of tools and proves the proposed method is effective.

Copyright © 2018 by ASME



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