Our image processing pipeline is meticulously designed to ensure accurate and efficient feature extraction, a critical step in identifying severe accidents. Leveraging deep learning models like ResNet and VGG, we analyze complex visual patterns and details to detect key indicators of accidents. Additionally, advanced edge detection techniques are employed to capture structural changes and sharp contrasts, helping to distinguish potential accident scenes from routine activity. Together, these methods create a robust system that interprets CCTV feeds in real time, enabling rapid, reliable detection in even challenging visual conditions.

Our model is designed to operate with minimal latency, ensuring rapid detection and response even in high-demand environments. Optimized for real-time processing, it delivers near-instantaneous identification of severe accidents, allowing dispatchers to be alerted without delay. The model's accuracy is bolstered by high-quality labeling practices, which enable it to differentiate accident scenarios effectively and consistently, minimizing false positives. This focus on low latency and precise labeling ensures that our technology performs reliably under real-world conditions, making quick, accurate assessments when every second counts.