|Table of Contents|

Collaborative Detection System for Distraction Behavior Based on Lightweight Network and Embedded Platform(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2023年01期
Page:
25-32
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Collaborative Detection System for Distraction Behavior Based on Lightweight Network and Embedded Platform
Author(s):
Li Shaofan1; 2; Gao Shangbing1; 2; Zhang Yingying1; 2; Huang Xiang1; Yang Suqiang1; Guo Xiaoyu1
(1.Faculty of Computer and Software Engineering,Huaiyin Institute of Technology,Huai'an 223001,China) (2.Laboratory for Internet of Things and Mobile Internet Technology of Jiangsu Province,Huaiyin Institute of Technology,Huai'an 223001,China)
Keywords:
collaborative detection; human object interaction; lightweight network; intelligent transportation; deep learning
PACS:
TP391
DOI:
10.3969/j.issn.1672-1292.2023.01.004
Abstract:
Distracted driving is the main cause of traffic accident. In order to solve the problems of fewer kinds of distracted driving detection and poor detection efficiency, a collaborative detection system for distraction behavior based on the lightweight network and embedded platform is proposed. First of all, a lightweight object detection network YOLO-Ghost is proposed by combining Ghost module and channel attention mechanism, the CSPGBottleck is proposed to build GhostDarknet as the backbone network, and a multi-feature fusion module SE-FPN with a multi-scale attention mechanism is proposed for feature fusion. A more comprehensive CIOU(complete-IOU)function is considered as the loss function. YOLO-Ghost is used to identify and locate local features, and APJ(anchor position judge)is proposed to judge manual distraction behavior. Secondly, MobileNetv3 and YOLO-Ghost are used to perform face key point regression and gaze estimation. Finally, the detected multimodal information is used to jointly determine the current driving state of the driver. The experimental results show that the YOLO-Ghost achieves the higher accuracy and speed than other main stream methods. At the same time, when the algorithm is deployed to the embedded device, it obtains 20FPS real-time detection performance on the NVIDIA Jetson TX1 and the accuracy and real-time performance reach the detection requirements.

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Last Update: 2023-03-15