UCA-YOLOv8n: a real-time and efficient fruit chunks detection algorithm for meal-assistance robot

Experimental results indicate that these enhancements substantially improve detection accuracy while reducing model size. Specifically, the optimized model achieves a 1.9 MB reduction in size, a decrease of 2.5 GFLOPs in parameter count, and an increase in mAP50 and mAP50-95 by 2.1% and 3.3%, respectively. The improved algorithm (UCA-YOLOv8n) enables real-time, accurate detection of various fruit chunks. Comparative analyses with other mainstream object detection algorithms further demonstrate the superiority and effectiveness of the proposed method.