Monocular 3-D Vehicle Detection Using a Cascade Network for Autonomous Driving

Three-dimensional object detection plays an important role in autonomous driving because it provides the 3-D locations of objects for subsequent use in decision-making modules. A novel method is proposed using a monocular image and cascade geometric constraints to achieve robust 3-D vehicle detection. The framework is divided into two stages. In the first stage, the monocular image input is processed using a heatmap-based detection network with five branches to regress the orientation, dimension, center projection of the bottom face, viewpoint classification, and 2-D bounding box. In the second stage, the intersection-over-union threshold is increased to filter out imprecise 2-D bounding boxes. Thereafter, cascade geometric constraints are used to obtain the final 3-D box output, which improves the detection performance under truncation and occlusion conditions. The proposed method is tested on the KITTI-3-D benchmark and is shown to be effective and efficient. The proposed framework does not depend on external sources or subnetworks and can be trained in an end-to-end manner.