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@article{161699,
        author = {B.Sravan Kumar Gandhi and E.Hemanth Reddy and G.Rahu Jaswanth and A.Nikhil and Dr.A.Kalavathi},
        title = {Object Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {5},
        pages = {414-417},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=161699},
        abstract = {Object identification is a fundamental computer vision task with many real-world applications, from driverless vehicles to surveillance systems. In order to attain real-time performance, this work provides an effective method for object detection that makes use of cutting-edge deep learning algorithms. By combining a lightweight convolutional neural network architecture with cutting-edge optimization methods, our approach produces a model that maintains good accuracy while drastically decreasing computing complexity. We perform in-depth tests on well-known object detection datasets to confirm the viability of our method and show that it is faster and more accurate than other approaches. The proposed model lays the way for implementing object identification in resource-constrained settings where real-time responsiveness is crucial. },
        keywords = {Computer vision,Object localisation,Classification,YOLO model,COCO dataset},
        month = {},
        }