Name
Abstract | ||
|---|---|---|
Intelligent traffic systems for railway object detection have become the focus of research in recent years. Accurate and fast object detection using a camera is an important but challenging problem in the railway industry. In this article, we propose an object detector with low power yet robust detection for collision warning in a train safety system. The proposed object detector comprises three modules. First, a stable sampling module is used to reduce the dimensions of the feature map and image information loss. Second, a lightweight feature extraction module utilizes a dynamic bottleneck structure to control the calculation load and enhance the expressive ability of the model. Third, a feature-fusion module combines high- and low-level features to enhance the semantic information and improve the accuracy of detection of multiscale and small objects. Experimental results demonstrate that the proposed network achieves reasonable results for railway object detection and outperforms the current state-of-the-art detectors. Finally, we design an obstacle-avoidance device that can be installed at the front of the train for real-time security warning in real-world conditions. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TIE.2020.3021640 | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS |
Keywords | DocType | Volume |
Rail transportation, Object detection, Feature extraction, Real-time systems, Cameras, Detectors, Roads, Deep learning, embedded platform, lightweight network, object detection, railway safety | Journal | 68 |
Issue | ISSN | Citations |
10 | 0278-0046 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |