关键词: 3D目标检测, 多尺度网络, 特征融合, 轴向注意力, 损失函数

Abstract: In 3D object detection, the detection accuracy of small targets such as pedestrians and cyclists remains low, presenting a challenging issue within perception systems of autonomous vehicles. To accurately estimate the state of surrounding environment and enhance driving safety, this study introduces an improved 3D object detection algorithm based on a multi-scale network and axial attention within the framework of Voxel-RCNN (Voxel Region-based Convolutional Neural Network). Firstly, a multi-scale network and a Pixel-level Fusion Module (PFM) were integrated into backbone network to obtain richer and more precise feature representations, enhancing robustness and generalization in complex scenarios. Secondly, an axial attention mechanism, tailored for 3D spatial feature dimensions, was applied to Region of Interest (RoI) multi-scale pooling features. This approach effectively captures both local and global features while preserving essential information in 3D spatial structure, thereby improving accuracy and efficiency of object detection and classification. Finally, incorporating a Rotation-Decoupled Intersection over Union (RDIoU) method into regression and classification branches, enabling network to learn more precise bounding boxes and overcoming alignment issues between classification and regression. According to experimental results on KITTI public dataset, the proposed algorithm achieved mean Average Precision (mAP) values of 62.25% for pedestrians and 79.36% for cyclists. These results represent improvements of 4.02 and 3.15 percentage points, respectively, compared to baseline algorithm, Voxel-RCNN. This demonstrates the effectiveness of improved algorithm in detecting hard-to-perceive targets.

Key words: 3D object detection, multiscale network, feature fusion, axial attention, loss function