Evaluating rain removal image processing solutions for fast and accurate object detection

Sammanfattning: Autonomous vehicles are an important topic in modern day research, both for the private and public sector. One of the reasons why self-driving cars have not yet reached consumer market is because of levels of uncertainty. This is often tackled with multiple sensors of different kinds which helps gaining robust- ness in the vehicle’s system. Radars, lidars and cameras are often the sensors used and the expenses can rise up quickly, which is not always feasible for different markets. This could be addressed with using fewer, but more robust sensors for visualization. This thesis addresses the issue of one particular failure mode for camera sensors, which is reduced view range affected by rainy weather. Kalman filter and discrete wavelet transform with bilateral filtering are evaluated as rain removal algorithms and tested with the state-of-the-art object detection algorithm, You Only Look Once (YOLOv3). Filtered videos in daylight and evening light were tested with YOLOv3 and results show that the accuracy is not improved enough to be worth implementing in autonomous vehicles. With the graphics card available for this thesis YOLOv3 is not fast enough for a vehicle to stop in time when driving in 110km/h and an obstacle appears 80m ahead, however an Nvidia Titan X is assumed to be fast enough. There is potential within the research area and this thesis suggests that other object detection methods are evaluated as future work.