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Recently, as many studies of autonomous vehicles have been achieved forlevels 4 and 5, there has been also increasing interest in the advancement ofperception, decision, and control technologies, which are the three majoraspects of autonomous vehicles. As for the perception technologies achievingreliable maneuvering of autonomous vehicles, object detection by using diversesensors (e.g., LiDAR, radar, and camera) should be prioritized. These sensorsrequire to detect objects accurately and quickly in diverse weather conditions,but they tend to have challenges to consistently detect objects in bad weatherconditions with rain, snow, or fog. Thus, in this study, based on theexperimentally obtained raindrop data from precipitation conditions, weconstructed a novel dataset that could test diverse network model in variousprecipitation conditions through the CARLA simulator. Consequently, based onour novel dataset, YOLO series, a one-stage-detector, was used toquantitatively verify how much object detection performance could be decreasedunder various precipitation conditions from normal to extreme heavy rainsituations.

Challenges of YOLO Series for Object Detection in Extremely Heavy Rain: CALRA Simulator based Synthetic Evaluation Dataset