An Efficient Hierarchical Convolutional Neural Network for Traffic Object Detection

In this paper, we propose a novel hierarchical convolutional neural network for traffic object detection, which is defined as Fusion and Multi-level Alignment CNN (namely FMLA-CNN). The method extends a popular two-stage detector by incorporating a remodified feature fusion module and a multi-level alignment (MLA) strategy such that it is capable of efficiently detecting multi-scale objects in autonomous driving scenario. The feature fusion strategy in proposal generation network improves detection accuracy by inserting high-level semantics to the whole pyramidal feature hierarchy. Subsequently the MLA strategy in the second detection stage can exactly reserve spatial locations from corresponding feature layers determined by hierarchical region-of-interest proposals. In the experiments on KITTI benchmark, our FMLA-CNN achieves an impressively better trade-off between accuracy and efficiency compared with other state-of-the-art methods.