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@article{170057,
        author = {Darshan C L and Sathya M and Krishna Prasanth S and Venkatramanan M},
        title = {Vehicle counting using YOLO},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {3055-3057},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170057},
        abstract = {The Vehicle counting is essential for traffic management and urban planning. YOLOv8, a state-of-the-art object detection model, offers real-time performance but faces challenges such as occlusion, lighting variations, and false detections in complex environments. This study examines YOLOv8's effectiveness and proposes improvements through fine-tuning with domain-specific datasets, adjusting confidence thresholds, and incorporating tracking algorithms like Deep SORT. Preprocessing techniques, such as background subtraction, further enhance detection accuracy. Although YOLOv8 shows promising results, optimizing hyperparameters and addressing environmental challenges are crucial for reliable vehicle counting. Future work can explore ensemble models and edge computing for better scalability in real-world scenarios.},
        keywords = {Vehicle counting, YOLOv8, traffic management, object detection, deep learning, Deep SORT, real-time monitoring, urban planning, predictive analytics, congestion control, ethical considerations, data privacy.},
        month = {November},
        }