Authors - S. T. Patil, Gaurav Sulsule, Urmila Kakarwal, Sanika Kolawale, Prathmesh Deshmukh Abstract - This paper suggests a deep learning-based solution for real-time detection of drowning and slipping accidents through computer vision. The system, which is grounded on the YOLOv8 (You Only Look Once) model, offers effective and efficient detection by analyzing video streams in real-time to detect dangerous incidents in settings such as swimming pools, building sites, and home homes. The system has a web-based user interface, real-time alerting capabilities, and SQLite database for storing data. The model was trained and tested with a large set of labeled images with an emphasis on balancing detection performance on frequent and infrequent incident classes. The results include robust detection performance with few false negatives and positives, fast response times, and effective processing of multiple video feeds. Despite problems with dataset imbalance and integration complexities, the system offers a cost-effective solution for enhancing safety, minimizing human error, and enhancing real-time monitoring capability. The research suggests the viability of AI-based solutions for safety-critical domains, with advantages of automated incident detection over conventional surveillance techniques.
