Road Condition Evaluation Using Fusion of Multiple Deep Models on Always-On Vision Processor

The aim of this work was to evaluate the usability of low-cost and low-power components for road condition classification and pedestrian detection in challenging environments. Therefore, deep learning based system dedicated for resource constrained environments is proposed to classify possible road hazards. In the performed experiments we investigated the influence of various factors (lightning conditions, moisture of the road surface and ambient temperature) on the system ability to accurately detect a person on the road. The implemented system was tested on images acquired in different climate zones. Preliminary results show that CNN models with optimized number of parameters can classify thermal road conditions with precision and recall above 70% in most cases, with exception of differentiating lightning conditions. This, in turn, proved system insensitivity to the poor lightning on the road and allows for evaluating road hazards regardless of the time of the day. Pedestrian detection using the model optimized for mobile environments proved to be highly efficient (precision and recall above 95%) both when ambient temperature is lower and higher than human body temperature. System was deployed on Movidius Neural Compute Stick, that allowed for increasing processing throughput (whole pipeline took 0.287±0.028s to process) while conserving energy, what indicates suitability of the solution for implementation in real-time automotive systems.