Better, Faster Small Hazard Detection: Instance-Aware Techniques, Metrics and Benchmarking

Vision-based detection of hazards in the path of ego-vehicle is a challenging task because of the variability in the type of hazards. In this paper, we present a detailed review of vision-based hazard detection methods followed by a set of new architectures and methods include semantic segmentation, instance segmentation, object detection, monocular vision with depth fusion based methods and ensembles. Additionally, we propose a set of new (and some old) benchmarking metrics that accurately capture the effectiveness of hazard detection algorithms, in terms of both algorithmic accuracy and deployability in vehicles. Detailed performance evaluations show that the proposed methods using Mask-RCNN, ensembles and monocular-stereo fusion surpass current state-of-the-art techniques in terms of accuracy and computational speed. Additionally, our fusion based object detection architectures provide a good tradeoff between accuracy (e.g. Average Precision) and computation requirements, with operating speeds that are 15 times faster than existing techniques.