PiSFANet: Pillar Scale-Aware Feature Aggregation Network for Real-Time 3D Pedestrian Detection

Detecting 3D pedestrian from point cloud data in real-time while accounting for scale is crucial in various robotic and autonomous driving applications. Currently, the most successful methods for 3D object detection rely on voxel-based techniques, but these tend to be computationally inefficient for deployment in aerial scenarios. Conversely, the pillar-based approach exclusively employs 2D convolution, requiring fewer computational resources, albeit potentially sacrificing detection accuracy compared to voxel-based methods. Previous pillar-based approaches suffered from inadequate pillar feature encoding. In this letter, we introduce a real-time and scale-aware 3D Pedestrian Detection, which incorporates a robust encoder network designed for effective pillar feature extraction. The Proposed TriFocus Attention module (TriFA), which integrates external attention and similar attention strategies based on Squeeze and Exception. By comprehensively supervising the point-wise, channel-wise, and pillar-wise of pillar features, it enhances the encoding ability of pillars, suppresses noise in pillar features, and enhances the expression ability of pillar features. The proposed Bidirectional Scale-Aware Feature Pyramid module (BiSAFP) integrates a scale-aware module into the multi-scale pyramid structure. This addition enhances its ability to perceive pedestrian within low-level features. Moreover, it ensures that the significance of feature maps across various feature levels is fully taken into account. BiSAFP represents a lightweight multi-scale pyramid network that minimally impacts inference time while substantially boosting network performance. Our approach achieves real-time detection, processing up to 30 frames per second (FPS).