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@article{170425,
        author = {Jyotirmay Singh Jaswal and Anandi Arora},
        title = {Object detection system using Deep learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {2445-2449},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170425},
        abstract = {A computer vision task called object detection involves locating various items in an image or video using bounding boxes and recognizing and categorising them. It combines techniques from image processing and machine learning to enable applications such as facial recognition, autonomous vehicles, and video surveillance. By analyzing visual data, object detection systems can accurately recognize various categories of objects, making them essential for numerous technological advancements. This paper presents the development of an advanced object detection system based on the YOLOv8 model architecture, optimized for real-time applications. By utilizing a custom dataset and a streamlined pipeline for data annotation, model configuration, and training, this system achieves accurate object detection with low latency. Key components include improvements in object localization and classification accuracy, achieved through parameter tuning and model optimization. Results demonstrate YOLOv8's effectiveness in diverse scenarios.},
        keywords = {object detection, YOLO, deep learning, computer vision, custom dataset, data annotation, bounding boxes, real-time detection, model optimization, transfer learning, feature extraction, loss monitoring},
        month = {December},
        }