자율주행을 위한 동적 객체 인식 방법에 관한 연구
A Study on the Motion Object Detection Method for Autonomous Driving
- Resource Type
- Article
- Authors
- 박승준 / Seung-jun Park; 박상배 / Sang-bae Park; 김정하 / Jung-ha Kim
- Source
- 한국산업융합학회 논문집 / Journal of Korean Society of Industry Convergence. Oct 31, 2021 24(5):547
- Subject
- Deep Learning
Faster R-CNN
Machine Learning
Support Vector Machine
Object Detection
Unmanned Vehicle
YOLO
- Language
- Korean
English
- ISSN
- 1226-833x
Dynamic object recognition is an important task for autonomous vehicles. Since dynamic objects exhibit a higher collision risk than static objects, our own trajectories should be planned to match the future state of moving elements in the scene. Time information such as optical flow can be used to recognize movement. Existing optical flow calculations are based only on camera sensors and are prone to misunderstanding in low light conditions. In this regard, to improve recognition performance in low-light environments, we applied a normalization filter and a correction function for Gamma Value to the input images. The low light quality improvement algorithm can be applied to confirm the more accurate detection of Object's Bounding Box for the vehicle. It was confirmed that there is an important in object recognition through image prepocessing and deep learning using YOLO.