A Hybrid Approach for Object Recognition and Abnormalities Detection Using CNN

This work focuses on developing a code that uses computer vision and machine learning techniques to detect abnormalities such as damaged cars, explosions, accidents, and indications of fire. To achieve this, we train our model using a dataset composed of earlier accident videos. The trained model can then be deployed to analyze the latest videos in real-time, without requiring human intervention. When an abnormality is detected, the system automatically sends alerts to the hospitals and police station providing them with the location information of the incident. Our team is trying to develop a real-time accident detection system that can find accidents as they occur and automatically alert emergency responders. The system will be designed to capture data from various sources, including sensors, cameras, and other connected devices, to detect real-life accidents. The main goal of our team is to build Accurate and fast detection of accidents and reliable and robust performance. reporting of accidents to emergency services, including location and severity of the incident, and integration with existing emergency response systems to ease a quick and effective response to accidents. The successful implementation of this system will have a significant impact on road safety, as it will enable prompt response to accidents, thereby reducing the risk of injuries and fatalities.