Road Traffic Condition And Fire Accident Monitoring Using Deep Learning

Traffic congestion and fire accidents are significant problems affecting both public safety and the economy. The present study introduces an advanced solution for road traffic condition and fire accident monitoring using deep learning. The system incorporates real-time monitoring, using sensors, cameras, and deep learning algorithms to detect and analyze traffic conditions as well as fire accidents. The proposed solution employs Convolutional Neural Networks (CNNs) to classify and predict road traffic conditions and fire incidents. By analyzing images and sensor data, the system automatically detects anomalies such as congestion and fire accidents, alerting authorities in real-time. This technology aims to improve traffic management, reduce response time to accidents, and enhance overall public safety. This work not only addresses the critical issues of traffic management and fire safety but also lays the groundwork for the development of smart city infrastructure that leverages advanced technologies to create safer, more efficient urban environments