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http://dx.doi.org/10.9708/jksci.2019.24.03.019

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

Yu, Jae-Hyoung (School of Electronic Engineering, Soongsil University)
Han, Youngjoon (Department of Smart Systems Software, Soongsil University)
Hahn, Hernsoo (School of Electronic Engineering, Soongsil University)
Publication Information
Journal of the Korea Society of Computer and Information / v.24, no.3, 2019 , pp. 19-27 More about this Journal
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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