An improved stamen target detection method based on YOLOv8

Aiming at the problem of difficulty in extracting key features caused by complex background information and many types of scenes and targets in the stamen image, the Multi-Head Self-Attention (MHSA) module is added to the YOLOv8 backbone network, which improves the ability of the backbone network to extract key features. To address the challenges of significant target scale variations and the presence of small targets in stamen images, we introduce a specialized small target detection layer. This enhancement allows the model to focus more on detecting small targets, thereby improving overall detection performance. By strengthening the YOLOv8 model's capabilities in small target detection and enabling efficient fusion of multi-scale and multi-layer features extracted by the backbone network, the detection performance is significantly enhanced. We conducted ablation experiments, and the experiment results show that the proposed algorithm improves the recognition precision and recall indexes by 2.7% and 2.9% respectively. Additionally, the mAP50 value reaches 99.3%, meeting the requirements for real-time accurate positioning of stamens.