标题： YOLOv8n 的高精度和轻量级糖尿病视网膜病变检测研究 显示英文标题

标题： Research on Improving the High Precision and Lightweight Diabetic Retinopathy Detection of YOLOv8n

摘要： 早期检测和诊断糖尿病视网膜病变是眼科当前的研究重点之一。 然而，由于微小病灶的特征细微且容易受到背景干扰，现有的检测方法在准确性和鲁棒性方面仍然面临许多挑战。 为了解决这些问题，提出了一种基于改进的YOLOv8n的轻量级高精度检测模型，命名为YOLO-KFG。 首先，设计了一种新的动态卷积KWConv和C2f-KW模块，以改进主干网络，增强模型感知微小病灶的能力。 其次，设计了一个面向特征的扩散金字塔网络FDPN，以充分整合多尺度上下文信息，进一步提高模型感知微小病灶的能力。 最后，设计了一个轻量级共享检测头GSDHead，以减少模型的参数数量，使其更易于在资源受限的设备上部署。 实验结果表明，与基础模型YOLOv8n相比，改进后的模型参数数量减少了20.7%，mAP@0.5提高了4.1%，召回率提高了7.9%。 与单阶段主流算法如YOLOv5n和YOLOv10n相比，YOLO-KFG在检测精度和效率方面都表现出显著优势。 显示英文摘要

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.