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@article{166592,
        author = {Mandeep Kaur},
        title = {vehicle detection using yolo algorithm in machine learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {2},
        pages = {1097-1106},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=166592},
        abstract = {Vehicle detection is a critical task in various domains including traffic surveillance, autonomous driving, and advanced driver assistance systems (ADAS). Traditional methods of vehicle detection, such as Haar cascades and Histogram of Oriented Gradients (HOG), have faced challenges related to scalability, robustness, and real-time application. The advent of machine learning, particularly deep learning, has significantly improved vehicle detection by leveraging models that can autonomously learn features from data. The YOLO (You Only Look Once) algorithm, with its unified neural network structure, excels in predicting bounding boxes and class probabilities in a single evaluation, making it highly efficient and suitable for real-time applications. YOLO's grid-based approach, combined with the use of anchor boxes and multi-scale features, enhances its capability to detect objects of varying sizes and aspect ratios, ensuring high speed and accuracy. This paper explores the application of the YOLO algorithm in vehicle detection, highlighting its performance in diverse scenarios and its potential for real-time traffic monitoring, autonomous driving, and urban planning.},
        keywords = {Vehicle detection, YOLO algorithm, Machine learning, Deep learning, Real-time applications, Traffic surveillance, Autonomous driving and ADAS},
        month = {July},
        }