Abstract
ABSTRACT Railway switches are vital for ensuring the safety of railroad transportation by managing the transitions between different tracks. We propose a skip feature enhanced multi-source fusion network with an attention mechanism for effectively identifying switch states. To further improve the network performance, we propose a data augmentation method of region horizontal splice with 16 images and a novel intersection over union loss function that takes into account the area and aspect ratio of both the predicted and ground truth boxes. Finally, a switch state dataset is built to test our model performance. With a default input size of 640 × 640 pixels, the proposed method achieves a precision of 87.1%, recall of 86.7%, and mAP of 89.4% which is 1.9%, 4.3%, and 1.8% better than Yolov5 baseline respectively. In addition, the inference speed of our proposed method in RTX 3070 is 50 FPS, which meets the requirements of real-time detection.
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