Real-time detection algorithm for edge computing platforms and remote sensing imagery
To address the issue of existing object detection algorithms struggling to meet real-time detection requirements in UAV remote sensing,we propose a model compression method based on ShuffleNetv2 and structured pruning.Using YOLOv5m as the foundation,we incorporate the ShuffleNetv2 model as the backbone network of YOLOv5m,reducing the model's parameter count and computational complexity while improving inference speed.Furthermore,we employ the ECA attention mechanism to replace the SE module in ShuffleNetv2,enhancing the feature extraction capability of the backbone network.Additionally,we adopt FocalEIoU as the loss function for the YOLOv5 algorithm,improving the model's regression ability.Finally,we use channel pruning to eliminate redundant parameters in the Neck structure,further compressing the model's parameters and computational complexity,and enhancing the pruned model's accuracy through fine-tuning.Experimental results show that,under the same testing environment,compared to YOLOv5m,the proposed model reduces the parameter count and floating-point operations by 86.3%and 80.0%,respectively.The model achieves an mAP@0.5 of 92%and an mAP@0.5:0.95 of 50.4%,outperforming other mainstream detection algorithms.Moreover,the proposed model achieves a detection speed of 35 frames/s on the AGX edge computing platform,satisfying the requirements for real-time detection.
万方数据
维普
