ETRI-Knowledge Sharing Plaform

ENGLISH

성과물

논문 검색
구분 SCI
연도 ~ 키워드

상세정보

학술지 Multi-Scale Detector for Accurate Vehicle Detection in Traffic Surveillance Data
Cited 38 time in scopus Download 17 time Share share facebook twitter linkedin kakaostory
저자
김광주, 김병근, 정윤수, 최두현
발행일
201906
출처
IEEE Access, v.7, pp.78311-78319
ISSN
2169-3536
출판사
IEEE
DOI
https://dx.doi.org/10.1109/ACCESS.2019.2922479
협약과제
19ZD1100, 대경권 지역산업 기반 ICT융합기술 고도화 지원사업, 문기영
초록
The recent research by deep learning has shown many breakthroughs with high performance that were not achieved with traditional machine learning algorithms. Particularly in the field of object detection, commercial products with high accuracy in the real environment are applied through the deep learning methods. However, the object detection method using the convolutional neural network (CNN) has a disadvantage that a large number of feature maps should be generated in order to be robust against scale change and occlusion of the object. Also, simply raising the number of feature maps does not improve performance. In this paper, we propose to integrate additional prediction layers into conventional Yolo-v3 using spatial pyramid pooling to complement the detection accuracy of the vehicle for large scale changes or being occluded by other objects. Our proposed detector achieves 85.29% mAP, which outperformed than those of the DPM, ACF, R-CNN, CompACT, NANO, EB, GP-FRCNN, SA-FRCNN, Faster-R CNN2, HAVD, and SSD-VDIG on the UA-DETRAC benchmark data-set consisting of challenging real-world-Traffic videos.
키워드
deep learning, machine learning, neural networks, object detection, occlusion, scale variation, traffic surveillance, UA-DETRAC benchmark, yolo
KSP 제안 키워드
Benchmark data, Commercial products, Convolution neural network(CNN), Data sets, Detection Method, Detection accuracy, Feature Map, High accuracy, High performance, Learning methods, Machine Learning Algorithms