AdaSP: Adaptive Soft Filter Pruning with Layer-Wise Ratios for Model Compression in Low-Altitude UAVs

The rapid development of the low-altitude economy is shaping industries such as transportation, logistics, and agriculture. The profound integration of artificial intelligence and low-altitude economic networks, particularly in unmanned aerial vehicles (UAVs), has created an expansive market for low-altitude applications, including logistics and aerial services. Deep learning technologies, including image classification, target recognition, and segmentation, have markedly improved UAVs' intelligent perception and operational capabilities. However, the deployment of deep learning models confronts substantial problems due to computing resource limits created by the storage space and maximum payload of UAVs. Filter pruning is an efficient model compression technique to reduce the dimensions of deep learning models and enhance inference performance. This paper introduces a soft filter pruning technique with adaptive layer pruning ratios, named AdaSP, which obviates the necessity for pre-training and fine-tuning, executing all computations through a "filter pruning-filter regrowing"process within a single training from scratch. Furthermore, we propose an algorithm based on updated weights for determining the real-time pruning ratio of each network layer. This algorithm does not require complete model training and does not rely on any additional dataset information. The optimal real-time pruning ratio for each layer can be computed during each training iteration. We compress the ResNet model for image classification tasks using ImageNet and remote sensing datasets (AID and NWPU45). AdaSP possesses a marginal advantage over alternative pruning techniques in enhancing top-1 accuracy and decreasing model FLOPs.