改进YOLOv8n实现轻量化高精度糖尿病视网膜病变检测
Research on Improving the High Precision and Lightweight Diabetic Retinopathy Detection of YOLOv8n
- 引入动态卷积与特征聚焦金字塔网络提升微病灶感知能力
- 参数减少20.7%，[email protected]提升4.1%，召回率提高7.9%
- 适合资源受限设备部署，适用于医疗图像实时筛查
糖尿病视网膜病变早期检测是眼科研究重点。由于微病灶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挑战。为此，提出一种基于改进YOLOv8n的轻量化高精度检测模型YOLO-KFG。首先设计新型动态卷积KWConv和C2f-KW模块，增强主干网络对微病灶的感知能力；其次构建特征聚焦扩散金字塔网络FDPN，充分融合多尺度上下文信息，进一步提升微病灶识别能力；最后设计轻量级共享检测头GSDHead，降低模型参数量，提升在资源受限设备上的可部署性。实验表明，相较于基线模型YOLOv8n，YOLO-KFG参数量减少20.7%，[email protected]提升4.1%，召回率提高7.9%。相比YOLOv5n、YOLOv10n等单阶段主流算法，该模型在检测精度与效率上均具显著优势。
原文摘要 · Abstract (English)
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 [email protected] 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.
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