Urban rail transit obstacle detection based on Improved R-CNN

Abstract

Excellent 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.