Fast Vehicle Detection with Lateral Convolutional Neural Network

Abstract

In this paper, we propose a fast vehicle detector for traffic surveillance. We first explore using different feature layers from a deep residual network to perform vehicle detection. Experiment results show that the high-resolution features from earlier feature layers contain more structural information, which is good to achieve fine-grained localization but yields low recall rates. The low-resolution features in the deep layers contain semantically strong information, which is good to represent the objectness but too coarse to achieve accurate localization. Therefore, we decouple the localization and objectness prediction from a single layer. Instead, we employ a lateral network that takes the features from earlier layers as input and outputs the localization residual. Our proposed detector can achieve fast detection at a rate of 28 frames/s, and a mean average precision (mAP) of 67.25% in the DETRAC vehicle detection benchmark.