Kavli Affiliate: Ran Wang
| First 5 Authors: Fei Yuhuan, Fei Yuhuan, , ,
| Summary:
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.
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