Lightweight Fresh Jujube Target Segmentation Algorithm Based on Improved Yolov8: Yoloseg-Jujube

Fruit instance segmentation algorithms are critical for target localization in fruit-picking robots, enabling accurate area estimation of fruits within images. However, existing methods often face limitations such as high computational cost, poor adaptability to low-power devices, and reduced detection performance in complex environments. To address these challenges, YOLOSeg-Jujube was proposed - an improved instance segmentation algorithm based on YOLOv8 - validated on a jujube image dataset annotated with bounding polygons. The architecture of YOLOSeg-Jujube was optimized through ablation studies to identify the most effective structure. The final network design includes Focus, CBS, Conv4cat, SPD, and SPPFr modules as the backbone, and a YOLOv8 head incorporating the SIoU loss function. Compared to YOLOv4-tiny, YOLOv5n, YOLOv7-tiny, and YOLOv8n, YOLOSeg-Jujube achieves reductions in parameter size of 62.8%, 9.8%, 73.9%, and 23.6%, respectively. The model achieves 83.5% B_mAP and 83.2% S_mAP, outperforming mainstream YOLO variants in both segmentation accuracy and area estimation of target objects. YOLOSeg-Jujube is robust, fast, and computationally efficient, making it suitable for deployment on resource-constrained platforms. Furthermore, it demonstrates strong potential for recognizing ripeness stages of fresh jujubes, providing technical support for intelligent harvesting systems in the field.