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An Anchor-free Convolutional Neural Network for Real-Time Surgical Tool Detection in Robot-assisted Surgery
2020
IEEE Access
Robot-assisted surgery (RAS), a type of minimally invasive surgery, is used in a variety of clinical surgeries because it has a faster recovery rate and causes less pain. Automatic video analysis of RAS is an active research area, where precise surgical tool detection in real time is an important step. However, most deep learning methods currently employed for surgical tool detection are based on anchor boxes, which results in low detection speeds. In this paper, we propose an anchor-free
doi:10.1109/access.2020.2989807
fatcat:mco6krlb3rfkhhclosf3tug6qa