Object Detection using Sensors and IOT to Prevent Accidents and Improve Road Safety

The current surge in vehicle-related accidents has underscored the critical need for advanced road safety systems. This study presents a comprehensive design and implementation of an IoT-based object detection system aimed at preventing accidents and enhancing road safety. The proposed system leverages a combination of sensors, including ultrasonic, infrared, and Pi Camera modules, to accurately detect and classify various obstacles such as vehicles, pedestrians, and animals in real-time. Advanced preprocessing techniques, specifically the Mean Abated Difference Improvement (MSDE) method, are employed to enhance image quality and segmentation accuracy. The detection and classification tasks are handled by a sophisticated deep learning-based Advanced Classifier, which processes the sensor data and provides high-precision object recognition. Upon detecting an obstacle, the system utilizes voice communication to alert the driver, prompting immediate corrective action to avoid potential collisions. The integration of a robust communication protocol ensures reliable data transmission between the sensor nodes and the central processing unit, even under high-interference conditions. The system's performance was rigorously tested through simulation and real-world experiments, demonstrating high accuracy, low latency, and robust operational reliability. This study employs an advanced classifier to accurately detect and identify objects in real time. Specifically, the system utilizes a deep learning model based on [specify architecture, e.g., Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN)], optimized for high accuracy in diverse road scenarios. This classifier is designed to process data from multiple sensors, enabling robust detection and timely decision-making to prevent accidents and enhance road safety. Calibration processes for the sensors ensured minimal deviations and high precision in distance measurements.