research-article

Railway Traffic Object Detection Using Differential Feature Fusion Convolution Neural Network

Authors:

Jie LiuAuthors Info & Claims

IEEE Transactions on Intelligent Transportation Systems, Volume 22, Issue 3

Pages 1375 - 1387

https://doi.org/10.1109/TITS.2020.2969993

Published: 01 March 2021 Publication History

Abstract

Railway shunting accidents, in which trains collide with obstacles, often occur because of human error or fatigue. It is therefore necessary to detect traffic objects in front of the trains and inform the driver to take timely action. To detect these objects in railways, we proposed an object-detection method using a differential feature fusion convolutional neural network (DFF-Net). DFF-Net includes two modules: the prior object-detection module and the object-detection module. The prior module produces initial anchor boxes for the subsequent detection module. Taking the initial anchor boxes as input, the object-detection module applies a differential feature fusion sub-module to enrich the sematic information for object detection, enhancing the detection performance, particularly for small objects. In experiments conducted on a railway traffic dataset, compared with the current state-of-the-art detectors, the proposed method exhibited significant higher performance and was more effective and more efficient than the other methods for object detection in railway tracks. Additionally, evaluation results based on PASCAL VOC2007 and VOC2012 indicated that the proposed method was significantly better than the state-of-the-art methods.

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Cited By

Yang BHu YLiu XLi J(2024)CEFusion: An Infrared and Visible Image Fusion Network Based on Cross-Modal Multi-Granularity Information Interaction and Edge GuidanceIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.342653925:11(17794-17809)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3426539
Cao ZQin YJia LXie ZGao YWang YLi PYu Z(2024)Railway Intrusion Detection Based on Machine Vision: A Survey, Challenges, and PerspectivesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.341217025:7(6427-6448)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3412170
Shi YZhao SWu JWu ZYan H(2024)Fixated Object Detection Based on Saliency Prior in Traffic ScenesIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.329505834:3(1413-1426)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/TCSVT.2023.3295058
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Index Terms

Railway Traffic Object Detection Using Differential Feature Fusion Convolution Neural Network
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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cover image IEEE Transactions on Intelligent Transportation Systems

IEEE Transactions on Intelligent Transportation Systems Volume 22, Issue 3

March 2021

611 pages

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1558-0016 © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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Published: 01 March 2021

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Cited By

Yang BHu YLiu XLi J(2024)CEFusion: An Infrared and Visible Image Fusion Network Based on Cross-Modal Multi-Granularity Information Interaction and Edge GuidanceIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.342653925:11(17794-17809)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3426539
Cao ZQin YJia LXie ZGao YWang YLi PYu Z(2024)Railway Intrusion Detection Based on Machine Vision: A Survey, Challenges, and PerspectivesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.341217025:7(6427-6448)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3412170
Shi YZhao SWu JWu ZYan H(2024)Fixated Object Detection Based on Saliency Prior in Traffic ScenesIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.329505834:3(1413-1426)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/TCSVT.2023.3295058
Rampriya RNathan SSuganya RPrathiba SPerumal PWang W(2024)Lightweight railroad semantic segmentation network and distance estimation for railroad Unmanned aerial vehicle imagesEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108620134:COnline publication date: 1-Aug-2024
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Yang SWang ZYu GLiu W(2023)Key Point Estimate Network for Rail-Track DetectionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.332799625:5(4077-4088)Online publication date: 7-Nov-2023
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Zhu LChen CWang HYu FTang T(2023)Machine Learning in Urban Rail Transit Systems: A SurveyIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.331913525:3(2182-2207)Online publication date: 5-Oct-2023
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Ding QLi PYan XShi DLiang LWang WXie HLi JWei M(2023)CF-YOLO: Cross Fusion YOLO for Object Detection in Adverse Weather With a High-Quality Real Snow DatasetIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.328503524:10(10749-10759)Online publication date: 1-Oct-2023
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Chen WMeng SJiang Y(2022)Foreign Object Detection in Railway Images Based on an Efficient Two-Stage Convolutional Neural NetworkComputational Intelligence and Neuroscience10.1155/2022/37496352022Online publication date: 1-Jan-2022
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Zheng ZLiu WLiu RWang LMao LQiu QLing G(2022)Anomaly Detection of Metro Station Tracks Based on Sequential Updatable Anomaly Detection FrameworkIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2022.318145232:11(7677-7691)Online publication date: 1-Nov-2022
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