Earticle

초록

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.

목차

Abstract
I. BACKGROUND
II. PROPOSED METHOD
A. Description of proposed technique
B. Overall system architecture
C. Procedure of the proposed method
III. EXPERIMENT
IV. CONCLUSION
REFERENCES

키워드

저자

• Odilbek Urmonov [ Electronics Engineering Department Chungbuk National University ]
• HyungWon Kim [ Electronics Engineering Department Chungbuk National University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004