Improving Performance of YOLO Network Using Multi-layer Overlapped Windows for Detecting Correct Position of Small Dense Objects

Jae-Hyoung Yu; Youngjun Han; Hernsoo Hahn

doi:https://doi.org/10.9708/jksci.2019.24.03.019

Journal of The Korea Society of Computer and Information

Abbr : JKSCI
2019, 24(3), pp.19-27
DOI :https://doi.org/10.9708/jksci.2019.24.03.019
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science

Jae-Hyoung Yu ¹ , Youngjun Han ² , Hernsoo Hahn ³

¹ 숭실대학교
² 숭실대학교
³ 숭실대학교

ABSTRACT

This paper proposes a new method using multi-layer overlapped windows to improve the performance of YOLO network which is vulnerable to detect small dense objects. In particular, the proposed method uses the YOLO Network based on the multi-layer overlapped windows to track small dense vehicles that approach from long distances. The method improves the detection performance for location and size of small vehicles. It allows crossing area of two multi-layer overlapped windows to track moving vehicles from a long distance to a short distance. And the YOLO network is optimized so that GPU computation time due to multi-layer overlapped windows should be reduced. The superiority of the proposed algorithm has been proved through various experiments using captured images from road surveillance cameras.
KEYWORDS

Multi-layer Overlapped Window , YOLO network , Small Dense Objects , Crossing Area , Small Vehicle Tracking
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KCI Citation (2)

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