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Vol. 32, No. 8(2), S&M2292

ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials, Volume 32, Number 6(1) (2020)
Copyright(C) MYU K.K.
pp. 1981-1995
S&M2234 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2020.2784
Published: June 10, 2020

Forward Collision Warning and Lane-mark Recognition Systems Based on Deep Learning [PDF]

Neng-Sheng Pai, Jing-Bin Huang, Jian-Xing Wu, Pi-Yun Chen, and Yue-Han Zhou

(Received December 18, 2019; Accepted April 22, 2020)

Keywords: deep learning (DL), object recognition, augmented reality (AR), YOLOv2, K-means

In this study, a driver assistance system that uses a network model based on deep learning technology was developed. It has forward collision warning and lane-mark recognition features. The application uses a webcam to capture forward images, which are transferred to a computer in which object recognition has been implemented. The system information is displayed on smart glasses through the network as an augmented reality image. You Only Look Once (YOLO) real-time object detection (tiny YOLOv2) was used as the main architecture to reduce the network complexity and enhance computing efficiency. During the training process, K-means was used to select the anchor box from each dataset. This enabled the size of the predicted box to be determined as a reference to enhance efficiency. This system makes it possible for the driver of a vehicle to learn about the movements and positions of vehicles ahead with respect to distance and lane marks. This reduces the chance of collisions as well as the violations of traffic regulations and improves driving safety.

Corresponding author: Neng-Sheng Pai


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This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Neng-Sheng Pai, Jing-Bin Huang, Jian-Xing Wu, Pi-Yun Chen, and Yue-Han Zhou, Forward Collision Warning and Lane-mark Recognition Systems Based on Deep Learning, Sens. Mater., Vol. 32, No. 6, 2020, p. 1981-1995.



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