Embedded multiple object detection based on deep learning technique for advanced driver assistance system

Fong An Chang, Chia Chi Tsai, Ching Kan Tseng, Jiun-In  Guo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

This paper proposes an optimized pedestrian and vehicle detection method based on deep learning technique. We optimize the convolutional neural network architecture by three mainly methods. The first one is the choice of the learning policy. The second one is to simplify the convolutional neural network architecture. The last one is careful choice of training samples. With limited loss of accuracy, we can greatly speed up the original deep learning method coming from CAFFE. The proposed system is developed on PCs and implemented on the platforms of both the PC and embedded systems. We can achieve around 90% accuracy when it is tested on an open-source dataset. On PCs with Intel i7@3.5GHz CPU, the proposed design can reach the performance about 720×480 video at 25 frames per second. On the NVIDIA JETSON TX1 embedded system, the proposed design can reach the performance about 720×480 video at 5 frames per second.

Original languageEnglish
Title of host publication2017 IEEE 60th International Midwest Symposium on Circuits and Systems, MWSCAS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages172-175
Number of pages4
ISBN (Electronic)9781509063895
DOIs
StatePublished - 27 Sep 2017
Event60th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2017 - Boston, United States
Duration: 6 Aug 20179 Aug 2017

Publication series

NameMidwest Symposium on Circuits and Systems
Volume2017-August
ISSN (Print)1548-3746

Conference

Conference60th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2017
CountryUnited States
CityBoston
Period6/08/179/08/17

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