IoT based Speed Control Monitoring and Accident Avoidance using AI Traffic Sign Detection

Traffic accidents are a major concern, often caused by driver errors such as speeding and distractions. This paper presents an IoT-based intelligent system for speed control monitoring and accident avoidance using AI-powered traffic sign detection. The proposed system leverages a Multitasking Convolutional Neural Network (MCNN) to detect traffic signs in real-time, analyze vehicle speed, and ensure compliance with road safety regulations. The system is designed to operate by capturing real-time road images using a vehicle-mounted camera, processing these images through an AI-driven recognition system, and integrating with a vehicle’s Electronic Control Unit (ECU) to monitor speed. Upon detecting a traffic sign, the system extracts its features, classifies it using MCNN, and determines the appropriate action. If a speed limit sign is detected and the vehicle exceeds the permitted speed, the system triggers an alert for the driver. If the violation persists, it automatically regulates the vehicle's speed through the ECU. Additionally, seat belt detection is incorporated using an interior camera to ensure driver safety. The system employs IoT for continuous data transmission, allowing remote monitoring and analysis. The combination of AI, IoT, and embedded control mechanisms enhances the effectiveness of traffic management and accident prevention. Experimental results demonstrate enhanced precision in recognizing traffic signs, emphasizing the capabilities of the system for deployment in smart transportation solutions.