Smart Accident Detection and Emergency Alert System -AI Powered: A Research Study

Abstract

This study introduces a new AI-based emergency detection and alerting application that will change passive CCTV surveillance into an intelligent and real-time safety solution. Conventional surveillance mechanisms are mostly reactive as they use human eyes and public reporting, thus resulting in slow responses in case of an emergency and low chances of saving lives in emergency situations. The suggested system takes advantage of the latest computer vision and deep learning solutions, such as the YOLO-based convolutional neural networks and dual-stream networks that can be used to detect fire, as well as vehicle crashes and fire incidents, automatically. The human-in-the-loop verification process will guarantee high accuracy because false positives will be minimized, but at the same time, the model can be improved constantly and enhanced through the integration of feedback. System performance is assessed based on a mix of publicly available benchmark datasets (CADP, FireNet) and about 3,500 real-world CCTV videos that were annotated by people. The advantages of the proposed approach over the use of manual monitoring, vision-only, and sensor-based systems are shown by such performance metrics as precision, recall, F1-score, false alarm rate, and detection latency. The efficiency gains and reliability improvements are also illustrated in the graphical results. The platform is scalable, can be deployed in the cloud, and will not require any extra hardware; therefore, it can be used on urban roads, highways, in smart cities, and in public spaces. This combined solution has a high practical and social impact by reducing the speed of emergency response, improving the safety of the populace, and offering law enforcement and first-line emergency agencies practical and digital evidence, which can be used.