Accurate 3D object detection from LiDAR data is vital for enhancing road safety, enabling efficient traffic management, and supporting reliable path planning in autonomous navigation systems. However, LiDAR point clouds suffer from inherent challenges such as sparsity, occlusion, and variations in point density, which can significantly impact detection accuracy. To address these challenges, we introduce 3DA-Net, a dual-attention-based network that integrates global and local context for enhanced 3D object detection. We begin by converting raw LiDAR point clouds into structured voxel representations, which are then processed through a hybrid dual-attention encoder. In this encoder, global attention modules capture high-level semantic dependencies across the entire scene, while local attention focuses on fine-grained geometric structures within neighborhoods. This dual-attention mechanism is further strengthened with point-wise and channel-wise attention, which enhances the model’s ability to capture both spatial and contextual information, which is essential for 3D perception. Our design incorporates a custom backbone for robust feature extraction from voxel-based pseudo-image representations, coupled with a feature pyramid network for efficient multi-scale feature learning. Evaluations on the KITTI dataset show that 3DA-Net achieves AP40 scores of 95.91% (easy), 94.78% (moderate), and 91.98% (hard) for cars in bird’s-eye view detection, and 95.83%, 94.58%, and 90.03% in 3D detection, outperforming strong LiDAR-based detection baselines. Significant improvements in pedestrian and cyclist detection further demonstrate the robustness and generalizability of our method in complex driving environments.
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