Tiny YOLO Optimization Oriented Bus Passenger Object Detection  

作　　者：ZHANG Shuo WU Yanxia MEN Chaoguang LI Xiaosong 

机构地区：[1]College of Computer Science and Technology, Harbin Engineering University

出　　处：《Chinese Journal of Electronics》2020年第1期132-138,共7页电子学报（英文版）

基　　金：supported by the National Key R&D Program of China (No.2016YFB1000402);the Natural Science Foundation of Heilongjiang Province(No.F2018008);the Foundation for Distinguished Young Scholars of Harbin(No.2017RAYXJ016);the Fundamental Research Funds for the Central Universities(No.3072019CFT0602)

摘　　要：The real-time collection of bus passenger object detection is an essential part of developing a smart bus system.The difficulty of object detection mainly lies in the objective factors,such as:clothing,hair style and accessories,light,etc.Traditional object detection met hods with the arti ficial feature ext rac tion suffers from insufficien t st reng th in expression,generalization,and recogni tion rate.The objec t detection met hod based on deep learning mainly uses the convol utio nal neural net work in deep learning to learn features from a large set of data.The learned features can describe the rich infbrmation inherent in the data,and improve the expression ability of the features as well as the recognition accuracy.Due to too many parameters of the Convolutional neural network(CNN)model,the amount of calculation is too large to be opera ted on the vehicle terminal.To reduce calculation burden and improve the operation speed,we employs the depthwise separable convolution method to optimize the convolutional layer of tiny YOLO net work model.It decomposes a complete convolution operation into depthwise convolution and pointwise convolution,thus reducing the parameter amount of the CNN and improving the operation speed.The experiment results reveal that the speed of bus passenger object detection detected by our improved model is 4 times faster than the previous one but with the nearly same detection accuracy.The real-time collection of bus passenger object detection is an essential part of developing a smart bus system. The difficulty of object detection mainly lies in the objective factors, such as: clothing, hair style and accessories, light, etc. Traditional object detection methods with the artificial feature extraction suffers from insufficient strength in expression, generalization, and recognition rate. The object detection method based on deep learning mainly uses the convolutional neural network in deep learning to learn features from a large set of data.The learned features can describe the rich information inherent in the data, and improve the expression ability of the features as well as the recognition accuracy. Due to too many parameters of the Convolutional neural network(CNN) model, the amount of calculation is too large to be operated on the vehicle terminal. To reduce calculation burden and improve the operation speed, we employs the depthwise separable convolution method to optimize the convolutional layer of tiny YOLO network model. It decomposes a complete convolution operation into depthwise convolution and pointwise convolution,thus reducing the parameter amount of the CNN and improving the operation speed. The experiment results reveal that the speed of bus passenger object detection detected by our improved model is 4 times faster than the previous one but with the nearly same detection accuracy.

关 键 词：Bus system Object detection Model optimization Tiny YOLO. 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术] U491.17[交通运输工程—交通运输规划与管理]