CMS R CNN: An Efficient Cascade Multi Scale Region based Convolutional Neural Network for Accurate 2D Small Vehicle Detection
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)
2019 Chinese Automation Congress (CAC)