Development of a Machine Learning-Based Model for an Intelligent Ambulance Detection System using CNN

One of the major challenges faced by ambulances is heavy traffic. When caught in congestion, ambulances can be delayed for longer periods, which poses a significant risk to patients' lives. To address this issue, a custom object detection system is developed to monitor ambulances approaching traffic signals known for congestion. When an ambulance is detected, we can minimize delays by activating green lights along its route. To implement this solution, we used a Raspberry Pi integrated with a camera. The Raspberry Pi runs OpenCV to process realtime traffic data and employs a Convolutional Neural Network (CNN) model to continuously detect ambulances in the incoming traffic footage. Once an ambulance is identified, the system triggers traffic control lights through a relay with a calculated time delay, successfully redirecting heavy traffic and allowing emergency vehicles to pass more freely. This approach has the potential to save many lives, as the system's ability (e.g., it's parameters like Anchor Box, etc.) to detect objects rapidly is a key advantage for real-time applications, such as ambulance detection. Moreover, its simple design and ensures reliability, making it a dependable tool in critical situations.