PredatorSense-Wildlife Detection System

This system explores the transformative applications of machine learning, specifically deep learning, in the domain of wildlife detection and alerting. It addresses two crucial domains: the automatic detection of animals in human-inhabited areas and the effective monitoring and protection of wild animals near forest boundaries. In human-inhabited areas, the presence of animals straying into densely populated regions poses significant security and safety challenges. This system provides valuable insights into the complexities of transfer learning, shedding light on the challenges encountered when adapting models trained on Google Open Images datasets in [2] to real-world deployment scenarios. Shifting its focus to wildlife conservation and protection near forest boundaries, this system underscores the critical need for effective monitoring. Efficient deep learning techniques enable the system to provide timely and reliable alerts, even in remote sensing areas where standard communication technologies may falter.It offers cost-effective solutions, mitigating the hardware implementation costs compared to existing systems [3], [4]. Our system demonstrates an 4.65% improvement over [1], [2] by effectively utilizing SSD MobileNet model for automatic animal detection addressing challenges in transfer learning and offering cost-effective solutions.