提高YOLOv8n高精度与轻量化的糖尿病视网膜病变检测研究
Research on Improving the High Precision and Lightweight Diabetic Retinopathy Detection of YOLOv8n
AI总结：
本文提出基于改进YOLOv8n的轻量化高精度糖尿病视网膜病变检测模型YOLO-KFG，通过动态卷积、特征聚焦扩散金字塔和轻量化共享检测头，在降低20.7%参数量的同时提升检测精度与召回率。
摘要：
糖尿病视网膜病变的早期检测与诊断是当前眼科学研究重点之一。然而，由于微病变特征细微且易受背景干扰，现有检测方法在准确性和鲁棒性方面仍面临诸多挑战。为解决这些问题，本文提出一种基于改进YOLOv8n的轻量化高精度检测模型，命名为YOLO-KFG。首先，设计了一种新的动态卷积KWConv和C2f-KW模块来改进骨干网络，增强模型感知微病变的能力。其次，设计了特征聚焦扩散金字塔网络FDPN，以充分融合多尺度上下文信息，进一步提升模型感知微病变的能力。最后，设计了轻量化共享检测头GSDHead，以降低模型参数量，使其更易于部署在资源受限设备上。实验结果表明，与基线模型YOLOv8n相比，改进后的模型参数量减少20.7%，mAP@0.5提升4.1%，召回率提高7.9%。与YOLOv5n和YOLOv10n等单阶段主流算法相比，YOLO-KFG在检测精度和效率方面均表现出显著优势。
英文摘要：
Early detection and diagnosis of diabetic retinopathy is one of the current research focuses in ophthalmology. However, due to the subtle features of micro-lesions and their susceptibility to background interference, ex-isting detection methods still face many challenges in terms of accuracy and robustness. To address these issues, a lightweight and high-precision detection model based on the improved YOLOv8n, named YOLO-KFG, is proposed. Firstly, a new dynamic convolution KWConv and C2f-KW module are designed to improve the backbone network, enhancing the model's ability to perceive micro-lesions. Secondly, a fea-ture-focused diffusion pyramid network FDPN is designed to fully integrate multi-scale context information, further improving the model's ability to perceive micro-lesions. Finally, a lightweight shared detection head GSDHead is designed to reduce the model's parameter count, making it more deployable on re-source-constrained devices. Experimental results show that compared with the base model YOLOv8n, the improved model reduces the parameter count by 20.7%, increases mAP@0.5 by 4.1%, and improves the recall rate by 7.9%. Compared with single-stage mainstream algorithms such as YOLOv5n and YOLOv10n, YOLO-KFG demonstrates significant advantages in both detection accuracy and efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
