CNN-Based Accident Detection and Real-Time Alert System for Intelligent Road Safety and Emergency Response Applications

The current study presents a CNN-based system for accident detection and alert generation, aimed at identifying road accidents from a video feed and sending out automatic alerts. The presented framework provides a unified solution that comprises video capturing, frame preprocessing, deep learning-based classification, and generating alerts. Based on the conducted experiments, the training accuracy is close to 98.2%, while the validation accuracy is around 87.8%. This suggests that the proposed framework can distinguish between accidents and regular traffic conditions and provide accurate results in most cases. It becomes evident that there is considerable potential in employing machine learning in road safety applications. In particular, the presented method reduces the need for constant human monitoring of surveillance video feeds and enables more efficient identification of accidents and generation of alerts, improving the chances for timely assistance to victims. All in all, the designed framework provides an efficient, cost-effective, and intelligent solution that can be utilized in existing traffic monitoring frameworks.

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