In the process of intelligent radar target detection,the real-time recognition of targets by edge devices is particularly important.Considering the resource constraints inherent in embedded devices,the need for lightweight target recognition networks has become increasingly prominent.To address the challenges of limited battlefield target images and low contrast,a battlefield low-altitude target recognition algorithm named YOLOF is proposed.This algorithm is based on the YOLOv5s network model and incorporates a cycle-generative adversarial network (CycleGAN) for image enhancement.By integrating the RepVGG module and SiLU activation function,the algorithm enhances the feature extraction capability and inference speed of model through structural reparameterization and more efficient activation functions.Additionally,a pruning algorithm based on filter importance is employed to accurately evaluate and remove the filters with low weight impact,thereby optimizing the model’s structure and improving the computational and storage efficiencies.Furthermore,the knowledge distillation based on feature layers allows the transfer of knowledge from the teacher model to the student model’s feature layers,thus maintaining the high performance of the pruned model.Experimental results demonstrate that the proposed YOLOF algorithm,compared to the original YOLOv5s algorithm,achieves network lightweighting while ensuring high-precision target recognition.Specifically,the parameter count is reduced to just 3.6×106,and the mean average precision (mAP) reaches 86.3% on a custom dataset,meeting the requirements for battlefield low-altitude target recognition.
