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영어

This paper focuses on the specific gaming scenario of Fruit Ninja and presents the design and implementation of an automated visual recognition and path control system for realtime fruit detection and automatic slicing. The system employs the YOLOv11s model trained on a publicly available Fruit Ninja screenshot dataset to achieve real-time detection of fruits and bombs. Building upon an open-source automated fruit-cutting project, this work introduces a lightweight path optimization module—DAFCS (Danger-Aware Fruit Cutting Strategy)— which dynamically generates safe and efficient slicing paths based on bomb locations and fruit distances. The overall system comprises object detection module, traking module, path planning module, mouse control for slicing execution, and hit evaluation module. Experimental results demonstrate that the DAFCS strategy, powered by YOLOv11s, significantly improves fruit hit rate and path efficiency compared to traditional sequential strategies using YOLOv8, while maintaining acceptable response speed. This system illustrates the practical value of integrating object detection and trajectory control techniques in interactive gaming scenarios and provides a valuable reference for future research on automated control in similar contexts.