An algorithm for highway vehicle detection based on convolutional neural network

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.