基于SqueezeNet卷积神经网络的车辆检测

doi:10.11959/j.issn.2096-3750.2020.00175

Abstract

Abstract:

In the intelligent transportation system,aiming at the problem of low portability and speed of detection in vehicle target detection algorithm,a vehicle detection method based on SqueezeNet convolutional neural network was proposed.In order to realize the rapid detection of vehicle targets,improve the portability and shorten the detection time of the single frame,the model was trained on the UA-DETRAC dataset by fusing the SqueezeNet with the single shot multibox detector (SSD) algorithm.The experimental results showed that the time of the single frame detection could reach 22.3 ms and the model size was 16.8 MB.Compared with the original SSD algorithm,the model size was reduced by about 8/9.At the same time,the accuracy of the proposed model was guaranteed.

Key words: intelligent transportation, convolutional neural network, SqueezeNet, vehicle detection

CLC Number:

TP389.1

Zefa WEI,Hua CUI. Vehicle detection based on SqueezeNet convolutional neural network[J]. Chinese Journal on Internet of Things, 2020, 4(3): 120-125.

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模型名称	模型大小/MB	训练时间/h	单张测试时间/ms	准确率
SSD-VGG16	95.5	65	30.6	89.2%
SSD-MobileNet	22.2	57	26.3	88.7%
SSD-ShuffleNet	14.5	36	16.1	82.4%
SSD-SqueezeNet	11.6	42	18.2	88.14%

模型名称	模型大小/MB	训练时间/h	单张测试时间/ms	准确率
SSD-SqueezeNet	11.6	42	18.2	88.14%
SSD-SqueezeNet-R	16.8	50	22.3	90.63%

Vehicle detection based on SqueezeNet convolutional neural network

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