An algorithm for highway vehicle detection based on convolutional neural network

EURASIP Journal on Image and Video Processing

Feiyue Ye¹  Honghui Fan¹  Qimei Chen²  Linkai Chen²  Yaduan Ruan² 

[1] School of Computer and Engineering, Jiangsu University of Technology;School of Electronic Science and Engineering, Nanjing University;

关键词: Vehicle detection;    Convolution neural network;    k-means;    Feature concatenate;   

DOI  :  10.1186/s13640-018-0350-2

来源: DOAJ

【 摘 要 】

Abstract In this paper, we present an efficient and effective framework for vehicle detection and classification from traffic surveillance cameras. First, we cluster the vehicle scales and aspect ratio in the vehicle datasets. Then, we use convolution neural network (CNN) to detect a vehicle. We utilize feature fusion techniques to concatenate high-level features and low-level features and detect different sizes of vehicles on different features. In order to improve speed, we naturally adopt fully convolution architecture instead of fully connection (FC) layers. Furthermore, recent complementary advances such as batch-norm, hard example mining, and inception have been adopted. Extensive experiments on JiangSuHighway Dataset (JSHD) demonstrate the competitive performance of our method. Our framework obtains a significant improvement over the Faster R-CNN by 6.5% mean average precision (mAP). With 1.5G GPU memory at test phase, the speed of the network is 15 FPS, three times faster than the Faster R-CNN.

【 授权许可】

Unknown