Convolutional Neural Network for Person Detection using YOLO Framework

Authors

  • M. H. Putra Centre for Telecommunication Research and Innovation (CeTRI), Faculty of Electronics and Computer Engineering (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Melaka, Malaysia
  • Z. M. Yussof Centre for Telecommunication Research and Innovation (CeTRI), Faculty of Electronics and Computer Engineering (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Melaka, Malaysia
  • S. I. Salim Centre for Telecommunication Research and Innovation (CeTRI), Faculty of Electronics and Computer Engineering (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Melaka, Malaysia
  • K. C. Lim Centre for Telecommunication Research and Innovation (CeTRI), Faculty of Electronics and Computer Engineering (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Melaka, Malaysia

Keywords:

ADAS, CNN, FPPI, YOLO,

Abstract

In this paper we present a real-time person detection system suitable for use in Intelligent Car or Advanced Driver Assistance System (ADAS). The system is based on modified You only Look Once (YOLO) which uses 7 convolutional neural network layers. The experimental results demonstrate that the accuracy of the person detection system is reliable for real time operation. The performance of the detection is benchmarked using the standard testing datasets from Caltech and measured using Piotr’s Matlab Toolbox. The results benchmarking is emphasizing on the performance of false positive per image (FPPI) over the miss rate. ADAS demands both relatively good detection and accuracy in order to work in real time operation. A good detection result is marked by achieving low miss rate and low FPPI. This requirement was achieved by the modified YOLO with 28.5%, 26.4% and 22.7% miss rate at 0.1 FPPI and believed to be an excellent candidate for use in ADAS.

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Published

2017-09-27

How to Cite

Putra, M. H., Yussof, Z. M., Salim, S. I., & Lim, K. C. (2017). Convolutional Neural Network for Person Detection using YOLO Framework. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-13), 1–5. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2557

Issue

Vol. 9 No. 2-13: Research and Innovation towards Sustainability in Electronic Engineering

Section

Articles

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