ETRI-Knowledge Sharing Plaform

KOREAN
논문 검색
Type SCI
Year ~ Keyword

Detail

Journal Article Multi-Scale Detector for Accurate Vehicle Detection in Traffic Surveillance Data
Cited 67 time in scopus Download 132 time Share share facebook twitter linkedin kakaostory
Authors
Kwang-Ju Kim, Pyong-Kun Kim, Yun-Su Chung, Doo-Hyun Choi
Issue Date
2019-06
Citation
IEEE Access, v.7, pp.78311-78319
ISSN
2169-3536
Publisher
IEEE
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.1109/ACCESS.2019.2922479
Abstract
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.
KSP Keywords
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
This work is distributed under the term of Creative Commons License (CCL)
(CC BY NC ND)
CC BY NC ND