  Surgical tool detection is a fundamental task for understanding egocentric
open surgery videos. However, detecting surgical tools presents significant
challenges due to their highly imbalanced class distribution, similar shapes
and similar textures, and heavy occlusion. The lack of a comprehensive
large-scale dataset compounds these challenges. In this paper, we introduce
EgoSurgery-Tool, an extension of the existing EgoSurgery-Phase dataset, which
contains real open surgery videos captured using an egocentric camera attached
to the surgeon's head, along with phase annotations. EgoSurgery-Tool has been
densely annotated with surgical tools and comprises over 49K surgical tool
bounding boxes across 15 categories, constituting a large-scale surgical tool
detection dataset. EgoSurgery-Tool also provides annotations for hand detection
with over 46K hand-bounding boxes, capturing hand-object interactions that are
crucial for understanding activities in egocentric open surgery.
EgoSurgery-Tool is superior to existing datasets due to its larger scale,
greater variety of surgical tools, more annotations, and denser scenes. We
conduct a comprehensive analysis of EgoSurgery-Tool using nine popular object
detectors to assess their effectiveness in both surgical tool and hand
detection. The dataset will be released at
https://github.com/Fujiry0/EgoSurgery.
