FENet: a feature enhancement network for LiDAR-based 3D object detection
Hongyi Guo, Jieyu Zhao · 2024
Accurate object detection in driving scenarios is critical for the safety and reliability of autonomous driving systems. However, detecting small objects such as pedestrians and cyclists poses significant challenges due to inadequate point cloud information. To address these challenges, we propose FENet, a Feature Enhancement Network designed to improve multi-class recognition, particularly for small objects. Feature Enhancement Network introduces two key modules: the Skeleton Point Sampling (SPS) module and the Geometry Knowledge Base (GKB) module. The Skeleton Point Sampling module optimizes the sampling process by selectively retaining skeleton points that are uniformly distributed across the object, preserving detailed shape information. This approach enhances the network’s ability to retain critical information about small objects. The Geometry Knowledge Base module automatically collects and stores high-quality geometric features, creating a comprehensive library of representative object shapes. When encountering weak features, the Geometry Knowledge Base module supplements them with geometrically similar features from the library, thus enriching the feature representation and improving the network’s robustness to occlusion and truncation. Experiments on the KITTI dataset demonstrate that Feature Enhancement Network significantly outperforms previous LiDAR-based methods in detecting small objects, achieving 61.26% AP for pedestrians and 75.95% AP for cyclists at the moderate difficulty level.