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@article{178465,
        author = {Dr. Preethi S and Rakshith Manjunatha and Renold Stephen R and Sam Melwin W and Vishnu Prasad R},
        title = {Real-Time Object Detection System Using YOLO and Raspberry Pi},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {5192-5200},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178465},
        abstract = {This paper presents a real-time object detection system designed to assist visually impaired individuals, leveraging the capabilities of YOLOv5 and the Raspberry Pi 4B. The system integrates a camera module for visual data acquisition, processed by the YOLOv5 algorithm to identify and classify objects in the user's surroundings. The Raspberry Pi 4B serves as the central processing unit, enabling efficient real-time analysis and minimizing latency. Complementary ultrasonic sensors are incorporated to provide distance information, enhancing spatial awareness and obstacle avoidance. The system aims to deliver timely auditory feedback, describing detected objects and their approximate distances, thereby improving navigation and situational awareness for blind users. The implementation focuses on optimizing YOLOv5 for the Raspberry Pi's computational constraints, ensuring a balance between detection accuracy and processing speed. Experimental results demonstrate the system's ability to accurately detect common objects in real-time, coupled with reliable distance measurement. This research contributes to the development of affordable and accessible assistive technologies for the visually impaired, promoting independent mobility and improved quality of life.},
        keywords = {Object Detection, YOLOv5, Raspberry Pi, Assistive Technology, Visual Impairment.},
        month = {May},
        }