Computer Science ›› 2019, Vol. 46 ›› Issue (7): 233-237.doi: 10.11896/j.issn.1002-137X.2019.07.035

• Graphics, Image & Pattern Recognition • Previous Articles     Next Articles

Lightweight SSD Network for Real-time Object Detection in Automotive Videos

ZHANG Lin-na1,CHEN Jian-qiang1,CHEN Xiao-ling1,CEN Yi-gang2,KAN Shi-chao2   

  1. (School of Mechanical Engineering,Guizhou University,Guiyan 550025,China)1
    (School of Computer Science & Information Technology,Beijing Jiaotong University,Beijing 100044,China)2
  • Received:2018-06-18 Online:2019-07-15 Published:2019-07-15

Abstract: Vehicle and pedestrian detection are the most basic and widely studied subjectin the field of advanced driver-assistance systems (ADAS).At present,deep learning achieved the best detection performance for object detection.However,the computational cost of deep learning algorithms is very high and the algorithms often require high perfor-mance GPU.In the real applications,object detection algorithm is required to be integrated into the vehicle hardware system.So the requirement of the hardware for the algorithm can not be too high.Based on the SSD network,a lightweight SSD network was proposed for real-time objection.By resizing the input images into a smaller size and significantly reducing the node number of the fully connected layer,the network complexity could be reduced.In addition,the object detection speed was improved.A supervised training method based on the multi-stage loss function was proposed to solve the problems of image deformation and the updated parameters in the VGG low layers caused by the shrink of the input images.Furthermore,because the detection accuracy of vehicles and pedestrians would be declined after the reduction of calculations,a hierarchical image partition method was proposed to expand the training dataset,which was able to solve the object vanishing problem caused by the image shrink.Experimental results show that the proposed lightweight SSD network not only realizes real-time vehicle and pedestrian detection on a laptop,but also maintains the detection accuracy.Compared with other object detection algorithms,the optimized network achieves faster detection speed for the vehicles and pedestrians.Also,the power consuming of the laptop is reduced significantly while the detection accuracy is the same.

Key words: Object detection, Deep learning, SSD, Advanced driver-assistance systems, Convolutional neural network

CLC Number: 

  • TP391.44
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