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Dangerous Scenes Recognition During Hoisting Based on Faster Region-Based Convolutional Neural Network

[+] Author Affiliations
Hongguo Su, Mingyuan Zhang, Shengyuan Li, Xuefeng Zhao

Dalian University of Technology, Dalian, China

Paper No. SMASIS2018-8226, pp. V002T05A013; 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


In the last couple of years, advancements in the deep learning, especially in convolutional neural networks, proved to be a boon for the image classification and recognition tasks. One of the important practical applications of object detection and image classification can be for security enhancement. If dangerous objects or scenes can be identified automatically, then a lot of accidents can be prevented. For this purpose, in this paper we made use of state-of-the-art implementation of Faster Region-based Convolutional Neural Network (Faster R-CNN) based on the monitoring video of hoisting sites to train a model to detect the dangerous object and the worker. By extracting the locations of them, object-human interactions during hoisting, mainly for changes in their spatial location relationship, can be understood whereby estimating whether the scene is safe or dangerous. Experimental results showed that the pre-trained model achieved good performance with a high mean average precision of 97.66% on object detection and the proposed method fulfilled the goal of dangerous scenes recognition perfectly.

Copyright © 2018 by ASME



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