earticle

영어

Autonomous driving relies on an accurate perception system that provides knowledge about surroundings and ensures safe driving performance. Usually, the perception system takes input information from onboard sensors (camera, LIDAR, RADAR, etc.) and then uses it to perform object detection tasks to accurately determine objects such as pedestrians, vehicles, traffic signs, and road barriers located around the ego vehicle. In order to have a safe trip and maneuver on the road, a vehicle detection algorithm should constantly improve the accuracy of vehicle detection. Since most of the conventional deep learning methods for vehicle or object detection rely on offline training with human-labeled large datasets, the conventional training methods have serious limitations in developing a breakthrough technique for gradual improvement in the detection accuracy of deep learning models. Thus, we propose a self-supervised training (SST) scheme that can gradually enhance detection accuracy with pseudo labeling.