<ul><li>Advancements in deep learning methods have led to the inclusion of advanced machine learning algorithms in autonomous systems.</li><li>This research focuses on developing a machine learning pipeline for object detection and tracking by creating a new dataset and refining it for accuracy.</li><li>The dataset was trained on YOLOv4 and Mask R-CNN models, achieving an average loss of 0.1942 and 96% accuracy.</li><li>Experimental results demonstrate the effectiveness of the models in accurately detecting and tracking objects, specifically a Roomba vacuum cleaner.</li></ul>

Exploring Machine Learning Engineering for Object Detection and Tracking by Unmanned Aerial Vehicle (UAV)

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