DETECTION OF SPEED BREAKERS, POT HOLES WHILE DRIVING

Y REDDEMMA Yagga; M Damodhar Reddy

DETECTION OF SPEED BREAKERS, POT HOLES WHILE DRIVING

Author(s)	Ms. Y REDDEMMA Yagga, Mr. M Damodhar Reddy
Country	India
Abstract	This paper focuses on developing a system for detecting speed breakers in real-time, leveraging advanced Python and computer vision techniques. The system uses the YOLO (You Only Look Once) object detection model to identify speed breakers in video feeds or images captured from a vehicle’s dashboard camera. By processing each frame of the video or an individual image, the system highlights the detected speed breakers with bounding boxes, providing visual cues to the driver. This system can be integrated into smart vehicle technologies to enhance driver safety by alerting them to upcoming road obstacles. It processes the input data using OpenCV for image handling and detection, and Flask is employed for creating a web interface that allows users to upload videos or images and receive processed outputs. The methodology ensures efficient and accurate detection, even in varied lighting and weather conditions, making it a robust solution for real-world applications. This technology can be instrumental in reducing accidents and improving driving experiences by ensuring that drivers are well-prepared for speed breakers, especially in unfamiliar areas. The objectives of the speed breaker detection system are to enhance driver safety by accurately detecting speed breakers in real-time from video feeds or images, thereby preventing accidents caused by sudden braking or unexpected obstacles. The system aims to improve the overall driving experience by providing visual alerts to drivers, allowing them to slow down smoothly and avoid vehicle damage or discomfort. It is designed to be robust, utilizing advanced computer vision techniques, such as the YOLO model, to ensure accurate detection across various lighting, weather, and road conditions. Additionally, the system is intended to be easily integrable with smart vehicle technologies, contributing to the development of autonomous driving systems.
Keywords	Computer vision, YOLO Model, Real time detection, Road obstacle detection, Image processing
Field	Engineering
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-04-08

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DETECTION OF SPEED BREAKERS, POT HOLES WHILE DRIVING

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