Detection Method of Railway Signal Lights and Parking Carriages Based onImproved Faster R-CNN

In railway shunting operation, the locomotives break the railroad switch and collide with parking carriage. Most of these accidents are caused by the misjudgment of drivers who cannot always observe the traffic lights on ground and parking carriages, owing to the special structure of the shunting locomotive. Aiming to settle the multi-scale object detection problem of railway signals and parking carriages, this paper improves the Faster R-CNN by using a fusion strategy of deep and shallow features and muti-scale training skills. Moreover, a large-scale dataset with annotated railway signals and parking carriages is constructed based on locomotive on-board videos. The experimental results demonstrate that on the built dataset the improved Faster R-CNN detector can achieve the detection accuracies of 96.6% and 98.9% on railway signal lights and parking carriages, respectively, and the detection efficiency reaches about 10 frames per second, which can meet the real-time requirements of low-speed shunting operation application scenarios in railway marshalling stations.