Road Traffic Condition And Fire Accident Monitoring Using Deep Learning
K. Manikanta, Bodapati Gayathri, Dudekula Shaheena, Jangam Chennakesava, Nallaiahgarinayanasree, Kammara Arunkumarachari
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.
