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Correction published on 10 July 2019, see Sensors 2019, 19(14), 3044.
Open AccessArticle

Automatic Railway Traffic Object Detection System Using Feature Fusion Refine Neural Network under Shunting Mode

by Tao Ye 1,*, Baocheng Wang 2, Ping Song 3 and Juan Li 3
1
Beijing Institute of Remote Sensing and Equipment, 52 Yongding Road, Haidian District, Beijing 100039, China
2
College of Computer and Science Technology, North China University of Technology, 5 Jin Yuan Zhuang Road, Shijingshan District, Beijing 100144, China
3
School of Instrumentation Science and Opto-Electronics Engineering, Key Laboratory of Precision Opto-Mechatronics Technology, Ministry of Education, Beihang University, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(6), 1916; https://doi.org/10.3390/s18061916
Received: 10 May 2018 / Revised: 4 June 2018 / Accepted: 8 June 2018 / Published: 12 June 2018
(This article belongs to the Section Physical Sensors)
Many accidents happen under shunting mode when the speed of a train is below 45 km/h. In this mode, train attendants observe the railway condition ahead using the traditional manual method and tell the observation results to the driver in order to avoid danger. To address this problem, an automatic object detection system based on convolutional neural network (CNN) is proposed to detect objects ahead in shunting mode, which is called Feature Fusion Refine neural network (FR-Net). It consists of three connected modules, i.e., the depthwise-pointwise convolution, the coarse detection module, and the object detection module. Depth-wise-pointwise convolutions are used to improve the detection in real time. The coarse detection module coarsely refine 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 on the railway traffic dataset show that FR-Net achieves 0.8953 mAP with 72.3 FPS performance on a machine with a GeForce GTX1080Ti with the input size of 320 × 320 pixels. The results imply that FR-Net takes a good tradeoff both on effectiveness and real time performance. The proposed method can meet the needs of practical application in shunting mode. View Full-Text
Keywords: shunting mode; feature fusion refine neural network; depthwise-pointwise convolution; effectiveness and real time shunting mode; feature fusion refine neural network; depthwise-pointwise convolution; effectiveness and real time
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Ye, T.; Wang, B.; Song, P.; Li, J. Automatic Railway Traffic Object Detection System Using Feature Fusion Refine Neural Network under Shunting Mode. Sensors 2018, 18, 1916.

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