Deep learning for object detection under rainy conditions - Chin, Zhuo Sheng (2018)
Fast, robust and accurate object detection are required for autonomous driving. While the main technology used for obstacle detection and avoidance is RADAR and LIDAR, LIDAR is affected by rain and snow, while RADAR resolution is low due to its longer wavelength. Vision based object detection is widely adopted because it is cost-effective and have a wide field-of-view. Unfortunately, current object detectors are not designed for adverse weather conditions. The performance of existing vision-based object detection methods, e.g. Mobileye, drops significantly under rainy conditions. This motivates us to develop an object detector which is robust to heavy rain. Furthermore, object detection developed in this report can be used to supplement RADAR and improve accuracy.