Abstract

Snake interactions can pose serious dangers to public safety and biodiversity preservation in both human-populated areas and animal environments. The creation of a Snake Detection From Video Footage and Alert System utilizing AI and ML technologies has attracted interest as a solution to this problem. The system attempts to reliably identify snakes and issue timely alerts, reducing human-snake conflicts and improving coexistence. It does this by utilizing real-time video processing, object detection techniques like YOLO, and intelligent decisionmaking. Current studies and projects in this area have shown that it is possible to identify snakes in video footage using machine learning techniques. Many researchers have used deep learning techniques, such as YOLO, to identify snakes in real time and with accuracy. The ability of contemporary systems to adapt to various situations, nevertheless, is still a study area that needs further attention. Real-time processing requirements may also encounter difficulties in contexts with limited resources. This gap will be filled by the proposed project's adaptive Real-Time Snake Detection with Alert Systems Using Deep Learning. Real-time snake recognition and alarm creation from a model's video feed are among the goals. With the help of these goals, the project hopes to provide a thorough and useful solution that will aid in the protection of both humans and wildlife in areas where snakes are a common occurrence