A Lightweight Object Detection Network for Real-Time Detection of Driver Handheld Call on Embedded Devices. (15th December 2020)

• Record Type: Journal Article
• Authors: Zhao, Zuopeng; Zhang, Zhongxin; Xu, Xinzheng; Xu, Yi; Yan, Hualin; Zhang, Lan
• Other Names: De Maio Carmen Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2020(2020)
• Publication Date: 2020-12-15
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2020/6616584
• Languages: English
• ISSNs: 1687-5265

Abstract

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.