A Lightweight Object Detection Network for Real-Time Detection of Driver Handheld Call on Embedded Devices
Joint Authors
Xu, Xinzheng
Zhao, Zuopeng
Zhang, Zhongxin
Yan, Hualin
Zhang, Lan
Xu, Yi
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-15
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
It is necessary to improve the performance of the object detection algorithm in resource-constrained embedded devices by lightweight improvement.
In order to further improve the recognition accuracy of the algorithm for small target objects, this paper integrates 5 × 5 deep detachable convolution kernel on the basis of MobileNetV2-SSDLite model, extracts features of two special convolutional layers in addition to detecting the target, and designs a new lightweight object detection network—Lightweight Microscopic Detection Network (LMS-DN).
The network can be implemented on embedded devices such as NVIDIA Jetson TX2.
The experimental results show that LMS-DN only needs fewer parameters and calculation costs to obtain higher identification accuracy and stronger anti-interference than other popular object detection models.
American Psychological Association (APA)
Zhao, Zuopeng& Zhang, Zhongxin& Xu, Xinzheng& Xu, Yi& Yan, Hualin& Zhang, Lan. 2020. A Lightweight Object Detection Network for Real-Time Detection of Driver Handheld Call on Embedded Devices. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1138794
Modern Language Association (MLA)
Zhao, Zuopeng…[et al.]. A Lightweight Object Detection Network for Real-Time Detection of Driver Handheld Call on Embedded Devices. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1138794
American Medical Association (AMA)
Zhao, Zuopeng& Zhang, Zhongxin& Xu, Xinzheng& Xu, Yi& Yan, Hualin& Zhang, Lan. A Lightweight Object Detection Network for Real-Time Detection of Driver Handheld Call on Embedded Devices. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1138794
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138794