DeepReject and DeepRoad: Road Condition Recognition and Classification Under Adversarial Conditions

Hidetomo Sakaino

Abstract: This paper presents the integrated Deep Learning models for camera image semantic segmentation of road conditions with dry, wet, and snow types along with the rejection of adversarial images. For drivers, road conditions are the most important visible factor for safe driving. Also, traffic control through remote cameras is required for sudden changes like dry to snow as well. However, adversarial images like lens reflection, strong light, and fog can impede normal driving and monitoring. Mostly, these are unpredictable to avoid. Using only simple pre- and post-image processing is limiting in alleviating or excluding such adversarial images. Most Deep Learning models have shown their performances in no or less adversarial images, i.e., fine weather in the daytime. Therefore, two Deep Learning models, DeepReject and DeepRoad, have been proposed to overcome such previous issues even under many adversarial events, i.e., strong lights and low light. Using various road conditions from dry to snow, experimental results have proven that the proposed models/methods outperform the previous single Deep Learning models in terms of stability, robustness, and accuracy. The proposed DeepReject and DeepRoad can contribute to warning drivers and supporting road maintainers for safety. Moreover, DeepReject is helpful whenever small image datasets for training many Deep Learning models are available.