Small, but important: Traffic light proposals for detecting small
  traffic lights and beyond
Tom Sanitz Christian Wilms Simone Frintrop
Traffic light detection is a challenging problem in the context of
self-driving cars and driver assistance systems. While most existing systems
produce good results on large traffic lights, detecting small and tiny ones is
often overlooked. A key problem here is the inherent downsampling in CNNs,
leading to low-resolution features for detection. To mitigate this problem, we
propose a new traffic light detection system, comprising a novel traffic light
proposal generator that utilizes findings from general object proposal
generation, fine-grained multi-scale features, and attention for efficient
processing. Moreover, we design a new detection head for classifying and
refining our proposals. We evaluate our system on three challenging, publicly
available datasets and compare it against six methods. The results show
substantial improvements of at least $12.6\%$ on small and tiny traffic lights,
as well as strong results across all sizes of traffic lights.