Abstract

Snake encountersin human-populated and wildlife areas pose significant threats to public safety and biodiversity. Each year, many incidents result in snakebites, often leading to serious injuries or fatalities, and frequently result in harm to snakes due to fear-driven responses. Traditional methods forsnake detection, such as visual observation, are typically slow and can lead to delayed or inaccurate responses, increasing the risks associated with human-snake encounters. This study presents a Real-Time Snake Detection and Alert System that employs advanced artificial intelligence (AI) and machine learning (ML) techniques, specifically using YOLO (You Only Look Once), to accurately identify the presence ofsnakes in pre-recorded videos or static images. Oursystem processes these visual inputs to determine if a snake is present, providing an immediate, automated alarm to alert individuals nearby if a snake is detected. This system isintended to be a rapid-response solution for detecting snakesin images or video files, enhancing safety in snake-prone areas by increasing awareness of potential threats without requiring constant live monitoring or additional notifications to authorities.