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@article{189299,
        author = {Usha H N and Bhumika N N and Kruthi R and Madhu and Rakshitha Y U},
        title = {Object Detection and Localization using YOLO Algorithm},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {7230-7235},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189299},
        abstract = {Road safety continues to be a critical concern worldwide, with thousands of accidents occurring due to delayed driver response and environmental hazards. Advanced Driver Assistance Systems have emerged as vital technologies to mitigate these risks through continuous environmental monitoring and intelligent decision-making. This paper presents the design and implementation of a cost-effective ADAS prototype that leverages the YOLO object detection algorithm integrated with an ESP8266 microcontroller-based robotic vehicle platform. The system incor- porates an ultrasonic sensor for proximity detection and utilizes IoT connectivity for remote monitoring and real-time alerts. By combining deep learning techniques with embedded hardware, this research demonstrates a practical approach to implementing collision avoidance, obstacle recognition, and automated warning systems. The proposed solution offers significant advantages in terms of affordability, scalability, and deployment feasibility for intelligent transportation applications.},
        keywords = {ADAS, YOLO Algorithm, Object Detection, ESP8266, IoT, Ultrasonic Sensor, Collision Avoidance, Embedded Systems, Autonomous Navigation},
        month = {December},
        }