WS-YOLO: Weakly Supervised Yolo Network for Surgical Tool Localization in Endoscopic Videos

Being able to automatically detect and track surgical instruments in endoscopic video recordings would allow for many useful applications that could transform different aspects of surgery. In robot-assisted surgery, the potentially informative data like categories of surgical tool can be captured, which is sparse, full of noise and without spatial information. We proposed a Weakly Supervised Yolo Network (WS-YOLO) for Surgical Tool Localization in Endoscopic Videos, to generate fine-grained semantic information with location and category from coarse-grained semantic information outputted by the da Vinci surgical robot, which significantly diminished the necessary human annotation labor while striking an optimal balance between the quantity of manually annotated data and detection performance. The source code is available at https://github.com/Breezewrf/Weakly-Supervised-Yolov8.