AI is employed in object detection to identify and locate objects within images or video. It utilizes deep learning techniques, such as convolutional neural networks (CNNs), to analyze visual data, detect objects of interest, and provide bounding box coordinates, enabling applications like autonomous driving, surveillance, and image recognition.
The research paper introduces DenseTextPVT, a method that uses the pyramid vision transformer (PvTv2) backbone to accurately detect dense text in scenes. It incorporates a deep multiscale feature refinement network (DMFRN) and pixel aggregation similarity vector methods to improve text detection and eliminate overlapping regions, outperforming previous methods on benchmark datasets.
Surrey University unveils a pioneering sketch-driven AI tool, promising to revolutionize object identification in images with wide applications, including transforming cancer diagnosis and aiding wildlife preservation initiatives.
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