Multimodal data fusion for object detection under rainy conditions - Liu, Ting Tao (Nanyang Technological University, 2022)
In recent years, autonomous driving technology has developed rapidly because there is demand to reduce number of the death caused by serious accident. As an important part of the autonomous driving perception algorithm, object detection algorithms have been given more and more attention and impressive progress has achieved. However, under rainy conditions, pure vision-based object detection methods can be severely affected, resulting in a large number of missed and wrong detections. At the same time, radar sensors are more robust to rain than camera sensors. Therefore, this project aims to implement a Multimodal Object Detection that combines radar and camera data for object detection in rainy weather conditions.