FGOD-YOLOv8: Fine-Grained Object Detection for Crops and Weeds

The task of fine-grained object detection aims to accurately detect small objects in images, which has become increasingly valuable in smart agriculture applications. Existing detection methods tend to have high false detection and missed detection when dealing with small objects such as crops and weeds, resulting in low accuracy. To address this issue, we proposed FGOD-YOLOv8, which consists of a cross-scale feature fusion module, a tiny object detection head, and a multi-scale dilated attention module. The cross-scale feature fusion module ensures effective transmission of high-level semantic information and low-level detail information. The tiny object detection head captures finer-grained features. The multi-scale dilated attention module focuses on object features at different scales to further improve accuracy. According to experimental results, our proposed method demonstrates better accuracy on cropandweed datasets compared to other approaches.