  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 are the first to present a network that performs joint
detection on traffic lights and signs. We measure our network on the
Tsinghua-Tencent 100K benchmark for traffic sign detection and the Bosch Small
Traffic Lights benchmark for traffic light detection and show it outperforms
the existing Bosch Small Traffic light state-of-the-art method. We focus on
autonomous car deployment and show our network is more suitable than others
because of its low memory footprint and real-time image processing time.
Qualitative results can be viewed at https://youtu.be/_YmogPzBXOw
