Cost efficient subcategory-aware CNN for object detection

Tingfeng Li, Xu Zhao

Abstract: In this paper, we propose an accurate and cost efficient deep CNN network for object detection. In contrast to the previous region-based methods like Sub-CNN [1], our detector is almost fully convolutional so that the computation cost can be reduced efficiently. By introducing position-sensitive score maps and exploiting subcategory information, our method is less time consuming while maintaining competitive performance on detecting objects with various scales. In addition, we remove image pyramid used in Sub-CNN to achieve further acceleration. The experimental results show that our approach is 1.3 times faster than Sub-CNN with only 14% number of parameters and archives comparable results on the challenging KITTI dataset. Compared with the state-of-the-art methods for object detection, our approach provides an efficient solution that takes into account both accuracy and speed.