Real-Time Tiger Detection Using Ml and Sensor Integration for Village Protection

Abstract

In this research, we addressed the critical issue of human-wildlife conflict, focusing on tigers entering villages near forested regions. Such incidents endanger human lives and livelihoods while also threatening wildlife conservation efforts. To mitigate this, we developed an Automated Tiger Detection System integrating motion sensors, ultrasonic sensors, thermal cameras, and machine learning algorithms. This system detects tiger presence in real time and sends alerts to villagers and authorities, enabling swift preventive action. We first discussed the limitations of traditional monitoring methods, such as their inability to provide timely or accurate alerts. We highlighted how modern AI-powered models like YOLO and Faster R-CNN improve detection accuracy. Additionally, we analysed the role of IoT and edge computing in real-time data processing, even in remote areas with limited connectivity. Our system was designed to be sustainable and energy-efficient, utilizing solar-powered components and low-power sensor modes. Our methodology involved assessing the needs of rural communities, developing a robust and modular architecture, and testing the system in field conditions. We also prioritized user-friendliness through intuitive dashboards and ensured compliance with wildlife and environmental regulations. Overall, our research demonstrates how technology can bridge the gap between human safety and wildlife conservation. By fostering coexistence, our system represents a significant step toward sustainable and harmonious living in areas where human and wildlife territories overlap.