Earticle

초록

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.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
III. METHODOLOGIES
A. Dataset and Model Training
B. System Architecture
C. Baseline Method: Sequential Connection Path Strategy
D. Proposed Strategy: DAFCS (Danger-Aware Fruit-Cutting Strategy)
E. Multi-frame Tracking and Hit Evaluation Mechanism
IV. EXPERIMENTS
A. Experimental Setup
B. Evaluation Metrics
C. Comparison Results
D. Visualization and Analysis
V. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Yanan Wang [ Department of Computer Science & Engineering Sejong University Seoul,Republic of Korea ]
• Wenqi Zhang [ Department of Computer Science & Engineering Sejong University Seoul,Republic of Korea ]
• L. Minh Dang [ Department of Computer Science & Engineering Sejong University Seoul,Republic of Korea ]
• Yue Zhang [ Department of Computer Science & Engineering Sejong University Seoul,Republic of Korea ]
• Muhammad Fayaz [ Department of Computer Science & Engineering Sejong University Seoul,Republic of Korea ]
• Jaemin Oh [ Department of Artificial Intelligence Data Science Sejong University Seoul,Republic of Korea ]
• Seong-wook Lee [ Department of Artificial Intelligence Data Science Sejong University Seoul,Republic of Korea ]
• Hyeonjoon Moon [ Department of Computer Science & Engineering Sejong University Seoul,Republic of Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004