Instance Segmentation Method for ‘Yuluxiang’ Pear at the Fruit Thinning Stage Based on Improved YOLOv8n-seg Model

Accurate detection and segmentation of young ‘Yuluxiang’ pear fruits at the fruit thinning stage are crucial for the development of intelligent fruit thinning robots. To address the challenges in recognition and segmentation of young ‘Yuluxiang’ pears in natural environments characterized by occlusion, overlap, and small targets, this paper proposes an improved instance segmentation model based on YOLOv8n-seg, named YOLOv8n-DSW. Firstly, the C2f modules were optimized by introducing DualConv to construct C2f-Dual modules, which enhanced feature extraction capability while reducing the number of parameters. Secondly, a Spatial-Channel Synergistic Attention (SCSA) mechanism was embedded ahead of the small-object detection head to improve detection accuracy for small targets. Finally, the original CIoU loss function was replaced with the WIoU v3 loss function to accelerate model convergence and improve accuracy. Deployment on a Firefly ROC-RK3588S-PC development board confirmed the model’s suitability for edge devices. Experimental results demonstrated that YOLOv8n-DSW achieved excellent performance. The mAP50, mAP75, and mAP50:95 for detection reached 95.6%, 83.2%, and 70.3%, respectively, and those for segmentation were 94.8%, 78.2%, and 65.3%. The proposed model outperformed its baseline, YOLOv8n-seg, as well as other classic models such as YOLOv5n-seg, YOLOv11n-seg, and YOLOv12n-seg. These results demonstrate that YOLOv8n-DSW provides accurate and efficient segmentation of young ‘Yuluxiang’ pear fruits.