Abstract
As algorithms get better at their accuracy and computational efficiency,
they invoke curiosity among the affected scientific communities to check
if they can benefit from newer versions or not. Computer vision is one
such domain that has observed rapid growth in terms of algorithmic
advancements. The advent of deep learning was itself a catalyst for
agile innovation. Coupled with rapid improvements in algorithms for
object detection, the speed of innovation has become tremendous. And so
there are many engineering and scientific disciplines that leverage
object detection, any improvement in the latter has a ripple effect on
the erstwhile. Computer Aided Laparoscopy (CAL) has come a long way due
to object detection algorithms based on deep learning. Yet, every once
in a while a new algorithm is released. It is tempting to see how a new
algorithm may have affected the tool detection accuracy and efficiency
in CAL. Recently version 8 of the famous You Look Only Once (YOLO)
algorithm was released. Like all the past releases, it has been claimed
that this version is better at detection accuracy as well as
computational efficiency. This paper examines the performance of YOLOv8
at tool detection in a CAL context. We employed a well-known laparoscopy
tool detection benchmark dataset in this research. Models with superior
performance have been obtained as a result of this research. Models are
superior in terms of both detection accuracy as well as inference speed.
Moreover, the models are ready to be deployed into a production
environment. The results that are reported in this paper are not only
useful for the surgical community but also for the benchmarking of the
YOLO algorithm.