HybridNet: A fast vehicle detection system for autonomous driving

For autonomous driving system, vehicle detection is an import part as well as a challenging problem due to the large intra-class differences caused by occlusion, truncation and different viewpoints. The detection system should be fast and accurate enough to support real-world applications. Most of the existing deep convolution neural network (CNN) based object detection methods can be roughly categorized into two streams: single-stage and two-stage modes. These single-stage methods are usually extremely fast and easy to train with losing some precision. As for two-stage methods, they often get high performance in object detection competitions, however, they are not competitive for real-world applications because of the speed limits. The detection system with high degree of precision and fast computation speed is desirable. In this paper, a new two-stage regression based cascade object detection system is proposed. This system can be fast detection of the vehicles which concentrated the advantages of the two aforementioned methods, denoted by HybridNet. In our design, the first and the second stage are both regression modes. We add a transitional stage to map proposals (generated in the first stage) on high resolution feature maps to get exact features for decision refinement in the second stage. The challenging KITTI and PASCAL VOC2007 data sets are used to evaluate our proposed method. The experimental results show that our approach is more fast and more accurate in vehicle detection than other state-of-the-art methods.