Pedestrian Detection Using Multispectral Images and a Deep Neural Network
1
College of Information Science and Engineering, Ritsumeikan University, Shiga 525-8577, Japan
2
Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Vijay John
Sensors 2021, 21(7), 2536; https://doi.org/10.3390/s21072536
Received: 9 March 2021
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Revised: 28 March 2021
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Accepted: 31 March 2021
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Published: 4 April 2021
(This article belongs to the Special Issue Sensor Fusion for Autonomous Vehicles)
Pedestrian fatalities and injuries most likely occur in vehicle-pedestrian crashes. Meanwhile, engineers have tried to reduce the problems by developing a pedestrian detection function in Advanced Driver-Assistance Systems (ADAS) and autonomous vehicles. However, the system is still not perfect. A remaining problem in pedestrian detection is the performance reduction at nighttime, although pedestrian detection should work well regardless of lighting conditions. This study presents an evaluation of pedestrian detection performance in different lighting conditions, then proposes to adopt multispectral image and deep neural network to improve the detection accuracy. In the evaluation, different image sources including RGB, thermal, and multispectral format are compared for the performance of the pedestrian detection. In addition, the optimizations of the architecture of the deep neural network are performed to achieve high accuracy and short processing time in the pedestrian detection task. The result implies that using multispectral images is the best solution for pedestrian detection at different lighting conditions. The proposed deep neural network accomplishes a 6.9% improvement in pedestrian detection accuracy compared to the baseline method. Moreover, the optimization for processing time indicates that it is possible to reduce 22.76% processing time by only sacrificing 2% detection accuracy.
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Keywords:
pedestrian detection; different lighting conditions; deep neural network; multispectral images; processing time
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MDPI and ACS Style
Nataprawira, J.; Gu, Y.; Goncharenko, I.; Kamijo, S. Pedestrian Detection Using Multispectral Images and a Deep Neural Network. Sensors 2021, 21, 2536. https://doi.org/10.3390/s21072536
AMA Style
Nataprawira J, Gu Y, Goncharenko I, Kamijo S. Pedestrian Detection Using Multispectral Images and a Deep Neural Network. Sensors. 2021; 21(7):2536. https://doi.org/10.3390/s21072536
Chicago/Turabian StyleNataprawira, Jason, Yanlei Gu, Igor Goncharenko, and Shunsuke Kamijo. 2021. "Pedestrian Detection Using Multispectral Images and a Deep Neural Network" Sensors 21, no. 7: 2536. https://doi.org/10.3390/s21072536
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