Real-Time Accident Event Detection Using CNN-YOLOv3 Fusion: Improving Accuracy for Enhanced Safety Measures

The importance of precise accident detection in enhancing personal safety and reducing overall impact cannot be overstated. This research introduces a unique method for detecting accidents that leverages Convolutional Neural Networks (CNN) and YOLOv3, a cutting-edge object detection algorithm. The integration of CNN and YOLOv3 aims to significantly improve real-time accident detection and classification capabilities. CNN is focused on categorizing known objects such as falls, while YOLOv3 precisely locates objects related to falls in the scene. This combination enables us to achieve an impressive accuracy rate of $98.67 \%$ in accident detection. This high level of accuracy is due to YOLOv3’s robust object detection capabilities and CNN’s expertise in classifying accidental events. A series of experiments using a comprehensive dataset have proven the effectiveness of this framework. The successful synergy between CNN and YOLOv3 yields excellent results, highlighting the potential to enhance safety measures in real-time accident scenarios.