TG-ADet: Terrain-Guided Network for 3-D Object Detection in ALS Point Clouds

Airborne laser scanning (ALS) offers significant potential for 3-D object detection due to its ability to penetrate the canopy and acquire high-precision 3-D spatial information. However, complex terrain distribution and backgrounds similar to objects hinder effective object detection in airborne scenes. To address these challenges, we propose TG-ADet, the first 3-D object detection network explicitly designed for ALS point clouds. Our approach introduces three key components and integrates them into a unified framework. A multistage terrain-guidance module predicts the terrain distribution and guides multiple detection stages based on prediction results, focusing on objects under various terrain conditions. A sparse feature enhancement (SFE) module that aggregates voxel features and leverages auxiliary tasks to improve the backbone’s feature representation and suppress background interference. In addition, an integrated data augmentation method generates training samples that align with ALS data distributions during network training, while increasing terrain complexity during testing. Experiments on two ALS point cloud datasets demonstrate that TG-ADet significantly outperforms state-of-the-art methods and achieves robust detection performance in challenging scenarios.