원문정보
초록
영어
In this study, the multi-lane detection problem is expressed as a CNN-based regression problem, and the lane boundary coordinates are selected as outputs. In addition, we described lanes as fifth-order polynomials and distinguished the ego lane and the side lanes so that we could make the prediction lanes accurately. By eliminating the network branch arrangement and the lane boundary coordinate vector outside the image proposed by Chougule’s method, it was possible to eradicate meaningless data learning in CNN and increase the fast training and performance speed. And we confirmed that the average prediction error was small in the performance evaluation even though the proposed method compared with Chougule’s method under harsher conditions. In addition, even in a specific image with many errors, the predicted lanes did not deviate significantly, meaningful results were derived, and we confirmed robust performance.
목차
1. 서론
2. CNN을 적용한 차선 검출 방법
2.1 차선 검출 방법
2.2 ShuffleNet
3. 차선 검출을 위한 데이터 세트 및 CNN 학습
3.1 데이터 세트 및 차선 좌표 설정
3.2 제안된 네트워크 구조와 CNN 학습 데이터
4. 모의실험 및 평가
4.1 네트워크 학습
4.2 평가
5. 결론
References