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Proceedings Paper

Shunting mode railway obstacles detection using feature fusion neural network
Author(s): Tao Ye; Juan Li
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Paper Abstract

Many railway accidents happen under shunting mode. In this mode, train attendants observe the railway condition ahead using the traditional manual method and tell the observation results to the driver to avoid danger. However, human error and fatigue will reduce the safety of shunting operation. To address this issue, a novel object detection framework for a train automatic detecting objects ahead in shunting mode, called Feature Fusion detection neural network (FFDet). It consists of two connected modules, i.e., the refine detection module and the object detection module. The refine detection module coarsely the locations and sizes of prior anchors to provide better initialization for the subsequent module and also reduces search space for the classification, whereas the object detection module aims to regress accurate object locations and predict the class labels for the prior anchors. The experimental results show that FFDet demonstrates good performance in detecting objects and can meet the needs of practical application in shunting mode.

Paper Details

Date Published: 12 December 2018
PDF: 17 pages
Proc. SPIE 10846, Optical Sensing and Imaging Technologies and Applications, 108460S (12 December 2018); doi: 10.1117/12.2503946
Show Author Affiliations
Tao Ye, Beijing Institute of Remote Sensing Equipment (China)
Juan Li, Beihang Univ. (China)

Published in SPIE Proceedings Vol. 10846:
Optical Sensing and Imaging Technologies and Applications
Mircea Guina; Haimei Gong; Jin Lu; Dong Liu, Editor(s)

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