基于混合注意力机制的轻量化骨折检测算法研究OA

Aiming at the challenges of fracture point localization and low detection accuracy in fracture detection scenarios,we propose a lightweight fracture detection model incorporating a hybrid attention mechanism,named FD-YOLO(Fracture Detection YOLO).Firstly,we design DepthwiseGroupConv(a groupwise separable convolution)and DWGAResidual(a groupwise expandable residual module)to construct the lightweight and efficient FGELAN module,which replaces the C2f module in the YOLOv8n backbone network.This enhances the feature extraction and channel adjustment capabilities of the backbone network while reducing model complexity.Sub-sequently,SPPELAN is used to replace the original SPPF module in YOLOv8n as the new spatial pooling module,which expands the re-ceptive field and further lightens the model.Then,the SELA hybrid attention mechanism is designed to focus on the channel and spatial expression capabilities of features,thereby enhancing the ability to capture key features.Finally,the feature extraction module FDB is de-signed,and the C2fFDB is constructed to reshape the neck network,thereby improving its feature processing capabilities.Experimental results demonstrate that on the public dataset GRAZPEDWRI-DX,compared with the baseline algorithm,the mAP50 and mAP50-95 metrics are increased by 3.4 percentage points and 1.9 percentage points,respectively,while the number of parameters and Flops are reduced by 0.2 G and 3.6%,respectively.Compared with other mainstream object detection algorithms,FD-YOLO is more suitable for the needs of fracture detection scenarios.