CMS R-CNN: An Efficient Cascade Multi-Scale Region-based Convolutional Neural Network for Accurate 2D Small Vehicle Detection

Abstract

In recent years, 2D vehicle detection has experienced impressive progress. Despite of the outstanding performance achieved by the state-of-the-art methods, it is still very challenging to detect vehicles in real scene due to the existence of small targets in images. In this work, we propose a Cascade Multi-Scale Region-based Convolutional Neural Network method (CMS R-CNN) for accurate small vehicles detection. Our method combines two key insights: (1) we propose a efficient multi-scale feature fusion network, which combines spatial information of feature maps with all scales, to enhance the ability to detect small vehicles; (2) we introduce a novel cascade framework which consists of a sequence of detectors trained with the increasing IoU thresholds strategy to further achieve better detection performance. Experiments show that our method achieves a high accuracy on the benchmark of KITTI dataset (77.0% in difficult data of KITTI) and outperforms previous work by a remarkable margin (6.2% higher than Faster R-CNN in difficult data of KITTI).