도심로 주행을 위한 딥러닝 기반 객체 검출 및 거리 추정 알고리즘 적용
Application of Deep Learning-based Object Detection and Distance Estimation Algorithms for Driving to Urban Area
0
조회
0
다운로드
4
인용
0
참고문헌
키워드
초록
본 논문은 자율주행 차량 적용을 위한 객체 검출과 거리 추정을 수행하는 시스템을 제안한다. 객체 검출은 최근 활발하게 사용되는 딥러닝 모델 YOLOv4의 특성을 이용해서 입력 이미지 비율에 맞춰 분할 grid를 조정하고 자체 데이터셋으로 전이학습된 네트워크로 수행한다. 검출된 객체까지의 거리는 bounding box와 homography를 이용해 추정한다. 실험 결과 제안하는방법에서 전반적인 검출 성능 향상과 실시간에 가까운 처리 속도를 보였다. 기존 YOLOv4 대비 전체 mAP는 4.03% 증가했다. 도심로 주행시 빈출하는 보행자, 차량 및 공사장 고깔(cone), PE드럼(drum) 등의 객체 인식 정확도가 향상되었다. 처리 속도는 약 55 FPS이다. 거리 추정오차는 X 좌표 평균 약 5.25m, Y 좌표 평균 0.97m으로 나타났다.
Abstract
This paper proposes a system that performs object detection and distance estimation for application to autonomous vehicles. Object detection is performed by a network that adjusts the split grid to the input image ratio using the characteristics of the recently actively used deep learning model YOLOv4, and is trained to a custom dataset. The distance to the detected object is estimated using a bounding box and homography. As a result of the experiment, the proposed method improved in overall detection performance and processing speed close to real-time. Compared to the existing YOLOv4, the total mAP of the proposed method increased by 4.03%. The accuracy of object recognition such as pedestrians, vehicles, construction sites, and PE drums, which frequently occur when driving to the city center, has been improved. The processing speed is approximately 55 FPS. The average of the distance estimation error was 5.25m in the X coordinate and 0.97m in the Y coordinate.
참고문헌 (9)
- Bochkovskiy, A., "Yolov4: Optimal speed and accuracy of object detection", arXiv preprint arXiv:2004.10934, ~ (2020)
- Cao, Y., "Estimating Depth from Monocular Images as Classification Using Deep Fully Convolutional Residual Networks", IEEE Transactions on Circuits and Systems for Video Technology, 28(11), pp.3174~3182 (2018)
- Elzayat, I. M., "Real-Time Car Detection-Based Depth Estimation Using Mono Camera", 30th International Conference on Microelectronics(ICM), pp.248~251 (2018), IEEE
- Kriegman, D., "Computer Vision I, Computer Science Engineering", pp.252~ (2007)
- Long, X., "PP-YOLO: An effective and efficient implementation of object detector", arXiv preprint arXiv:2007.12099, ~ (2020)
- Pan, S. J., "A Survey on Transfer Learning", IEEE Transactions on Knowledge and Data Engineering, 22(10), pp.1345~1359 (2010)
- Redmon, J., "YOLOv3: An Incremental Improvement", arXiv preprint arXiv:1804.02767, ~ (2018)
- Redmon, J., "You only look once: Unified, real-time object detection", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.779~788 (2016)
- Zhang, Y., "A Comprehensive Review of One-stage Networks for Object Detection", 2021 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), pp.1~6 (2021)