Abstract: Abstract: The autonomous and safe operation of airport special vehicles is essential to ensure the safety of the airfield area. At present， most airport special vehicle operations are mainly completed by driver’s operation and the visual command of controllers， in which such challenges as over-reliance on manpower and low autonomy. To improve its safety and autonomy， this paper presents a target recognition method for the airport environment based on the 3D point cloud segmentation. Firstly， a simulation-based approach is used to construct a point cloud dataset （Airfield Area of Airport Point Cloud Data，3A-PCD） of the airfield area environment. Secondly， based on PointNet++， a semantic segmentation network 3A-Net for large-scale point cloud data is designed， and a combined sampling point spatial encoding module and attentive pooling module are proposed to address the problem of traditional segmentation networks in terms of low segmentation accuracy and lack of ability to retain detailed features of objects. Finally， experiments were designed based on the 3A-PCD dataset， the ablation experiment result shows that the MIoU of the model increases by 6.0 percentage points with the addition of the spatial encoding module and by 3.9 percentage points with the addition of the AP module. 3A-Net achieves a 6.7 percentage points improvement in MIoU compared to the benchmark model PointNet++. In comparison with 6 existing advanced semantic segmentation models， the performance of the proposed model has been improved to varying degrees and is more suitable for target recognition in large outdoor scenes.
Key words：target recognition； airport special vehicle； semantic segmentation； attention mechanism； 3D scene simulation

Key words: Key words：target recognition, airport special vehicle, semantic segmentation, attention mechanism, 3D scene simulation