A Lightweight Object Detection Network for Real-Time Detection of Driver Handheld Call on Embedded Devices

Zuopeng Zhao; Zhongxin Zhang; Xinzheng Xu; Yi Xu; Hualin Yan; Lan Zhang

doi:10.1155/2020/6616584

A Lightweight Object Detection Network for Real-Time Detection of Driver Handheld Call on Embedded Devices

Comput Intell Neurosci. 2020 Dec 15:2020:6616584. doi: 10.1155/2020/6616584. eCollection 2020.

Authors

Zuopeng Zhao^{1

2}, Zhongxin Zhang^{1

2}, Xinzheng Xu^{1

2}, Yi Xu^{1

2}, Hualin Yan^{1

2}, Lan Zhang^{1

2}

Affiliations

¹ School of Computer Science and Technology & Mine Digitization Engineering Research Center of the Ministry of Education of the People's Republic of China, China University of Mining and Technology, Xuzhou 221116, China.
² School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China.

Abstract

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.

MeSH terms

Algorithms*
Neural Networks, Computer*
Recognition, Psychology