Monocular 3D Object Detection for Autonomous Driving Based on Contextual Transformer

Aiming at the current problems of leakage and poor multi-scale target detection in monocular 3D object detection, a monocular 3D object detection algorithm for autonomous driving based on Contextual Transformer (CM-RTM3D) is proposed. Firstly, Contextual Transformer (CoT) is introduced into the ResNet-50 network to construct the ResNet-Transformer architecture for feature extraction. Secondly, the multi-scale spatial perception (MSP) module is designed to improve the loss of shallow features through scale-space response operations, embedding the coordinate attention mechanism (CA) along both horizontal and vertical spatial directions, and generating soft weights of importance at each scale using the softmax function. Finally, the Huber loss function is used instead of the L1 loss function in the offset loss. The experimental results show that, compared with the RTM3D algorithm on the KITTI autopilot dataset, the algorithm in this paper improves AP3D by 4.84, 3.82, and 5.36 percentage points, and APBEV by 4.75, 6.26, and 3.56 percentage points, respectively, at the three difficulty levels of easy, medium, and difficult. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.