首页|Urban rail transit obstacle detection based on Improved R-CNN
Urban rail transit obstacle detection based on Improved R-CNN
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Elsevier
? 2022 Elsevier LtdExcellent active obstacle detection capability is critical to operate fully automatic trains safely and reliably. There are some problems exist in the traditional sensor-based obstacle detection approaches, such as low detection accuracy, sluggish detection speed and a limited number of obstacle types. In this work, a fast and accurate object detector termed improved R-CNN is proposed by introducing new up-sampling parallel structure and context extraction module (CEM) into the architecture of R-CNN. Furthermore, transfer learning is applied to inherit the COCO dataset's pre-training weight. The network is trained on track lines and test lines with nine types of obstacles. The data is evaluated and statistically cleansed, and the fine-tuning anchor improves the network's flexibility within the dataset. With the input size of 1330 px × 800 px, the test results show that the improved R-CNN model achieves an accuracy of 90.6% and a detection speed of 11 FPS. In comparison to other state-of-the-art detectors, the model has great performance in obstacle identification of rail track and achieves a good balance between detection speed and accuracy.
Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education School of Mechanical Engineering of Guangxi University Guangxi Key Laboratory of Disaster Prevention and Engineering Safety
CRRC Zhuzhou Electric Locomotive Institute Co. Ltd.
NSTL
Elsevier
