TrashHunter: A YOLOv8-Powered Mobile System for Gamified Litter Detection and Collection

CONCLUSION & FUTURE SCOPE   LitterScan successfully demonstrates that integrating YOLOv8-based real-time object detection with gamification mechanics creates an effective, engaging system for incentivizing litter collection. The 81.4% detection accuracy, 73% user retention, and 4.4/5 user satisfaction rating validate the approach. This research contributes a replicable framework for transforming environmental responsibility from passive awareness into active, rewarding participation through accessible mobile technology. Litter pollution remains one of the most visible and solvable environmental challenges of our time. The solution does not require technological breakthroughs—YOLOv8 already provides capable detection, modern mobile browsers can run real-time inference, and gamification principles are well-established. What has been missing is the integration of these capabilities into a coherent, accessible, and engaging system that empowers individual citizens to act. LitterScan demonstrates that this integration is not only possible but effective. A user with a smartphone and internet connection can now transform litter collection from an unpleasant chore into an engaging game—earning virtual coins, building combos, and receiving encouraging feedback while contributing to cleaner communities. The system's 73% retention rate suggests that gamification can sustain engagement beyond the novelty effect, addressing the fundamental challenge of long-term participation in environmental initiatives. When users return for second, third, and tenth sessions, the environmental impact multiplies accordingly.

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