Date of Publication :15th May 2024

Abstract:With the rapid advancements in computer vision and deep learning technologies, the integration of IoT (Internet of Things) has ushered in a new era for enhancing driving safety and reducing traffic accidents. While modern cars typically come equipped with integrated ADAS systems, there's a gap for vehicles lacking such built-in capabilities. This paper proposes a portable, image-based IoT system tailored for real-time detection of crucial elements such as traffic signs, vehicles, and pedestrians. To achieve seamless real-time detection, our system harnesses the power of the YOLO v8 algorithm. This algorithmic framework enables efficient processing of visual data, ensuring swift and accurate identification of pertinent objects on the road. By leveraging Io T, our system transcends the limitations of traditional ADAS setups, offering a flexible solution that can be easily deployed across diverse vehicle types. This single-stage detector is very popular as it has high detection speed and accuracy. This approach utilizes ultrasonic sound sensor technology to detect and convey messages to the driver regarding nearby vehicles approaching their car. It assesses the proximity between two vehicles traveling in the identical lane and direction, providing real-time feedback to the driver. Through the combination of IoT and advanced computer vision algorithms, our portable system empowers vehicles of varying makes and models with intelligent detection capabilities.

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