SFD-IAFNet: 3D Detection Method for Vehicle Small Objects Based on Multi-scale Feature Enhancement and Cross-modal Interlaced Attention

Abstract 3D object detection is critical for autonomous driving, enabling precise obstacle localization to support navigation and decision-making. However, detecting small objects remains challenging due to sparse point clouds and suboptimal multimodal fusion. To address these challenges, this paper proposes the Sparse Fuse Dense with Interlaced-Attention and Fusion Network (SFD-IAFNet), a novel multimodal fusion-based 3D object detection framework. First, building on the SFD network, to resolve the coarse feature extraction of pseudo point clouds, we design the multi-scale feature enhancement module VoxelFPN_CMLP module, voxel pyramid structure and compound multilayer perceptron are introduced, which can effectively alleviate the fuzzy problem of pseudo-point cloud in feature expression of long-distance targets, which deeply explores multi-level features of pseudo point clouds, significantly enhancing the feature representation of small objects. Second, for insufficient fusion and multimodal semantic alignment between raw and pseudo-point cloud features, we introduce the Interlaced-Attention mechanism, the mechanism takes bimodal features as input and realizes deep fusion between pseudo-point cloud and raw point cloud through two-layer cross-decoding, which significantly improves fusion accuracy and context semantic modeling ability. Additionally, a dynamic loss weighting strategy is introduced to prioritize hard samples during training. Experimental results on the KITTI dataset demonstrate that our method achieves 95.84% (easy), 88.90% (mod), and 87.67% (hard) in 3D detection accuracy, outperforming the baseline SFD model by 0.36%, 1.43%, and 2.29%, respectively. Ablation studies validate the efficacy of our modules, particularly in complex scenarios with occlusions or distant small objects.