Mapping Road Safety Barriers across Street View Image Sequences: A Hybrid Object Detection and Recurrent Model

Abstract

Road safety barriers (e.g., concrete barriers, metal crash barriers, rumble strips) play an important role in preventing or mitigating vehicle crashes. Accurate maps of road safety barriers are critical components of safety infrastructure management systems at federal or state transportation agencies. In current practice, mapping road safety barriers is largely done manually (e.g., driving on the road or visual interpretation of street view imagery), which is slow, tedious, and expensive. We propose a deep learning approach to automatically map road safety barriers from street view imagery. Our approach considers road barriers as long objects spanning across consecutive street view images in a sequence and use a hybrid object-detection and recurrent-network model. Preliminary results on real-world street view imagery show that the proposed model outperforms several baseline methods.