Traffic light and sign detectors on autonomous cars are integral for road scene perception. The literature is abundant with deep learning networks that detect either lights or signs, not both, which makes them unsuitable for real-life deployment due to the limited graphics processing unit (GPU) memory and power available on embedded systems. The root cause of this issue is that no public dataset contains both traffic light and sign labels, which leads to difficulties in developing a joint detection framework. We present a deep hierarchical architecture in conjunction with a mini-batch proposal selection mechanism that allows a network to detect both traffic lights and signs from training on separate traffic light and sign datasets. Our method solves the overlapping issue where instances from one dataset are not labelled in the other dataset. We show that our architecture, a single network, achieves competitive results when jointly detecting traffic lights and signs in both the difficult Tsinghua-Tencent 100K benchmark for traffic sign detection and Bosch Small Traffic Lights benchmark for traffic light detection.
"A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection"
Alex Pon, Oles Andrienko, Ali Harakeh, Steven Waslander.
Technical report (Xplore), 2018
Sample detections from the network described in the publication. We qualitatively test the network on a sequence of frames from difference cities than what the network was trained on. The model used was trained on the LISA sign and light datasets.