Abstract
Real-time and robust automatic detection of polyps from colonoscopy videos
are essential tasks to help improve the performance of doctors during this
exam. The current focus of the field is on the development of accurate but
inefficient detectors that will not enable a real-time application. We advocate
that the field should instead focus on the development of simple and efficient
detectors that an be combined with effective trackers to allow the
implementation of real-time polyp detectors. In this paper, we propose a Kalman
filtering tracker that can work together with powerful, but efficient
detectors, enabling the implementation of real-time polyp detectors. In
particular, we show that the combination of our Kalman filtering with the
detector PP-YOLO shows state-of-the-art (SOTA) detection accuracy and real-time
processing. More specifically, our approach has SOTA results on the
CVC-ClinicDB dataset, with a recall of 0.740, precision of 0.869, $F_1$ score
of 0.799, an average precision (AP) of 0.837, and can run in real time (i.e.,
30 frames per second). We also evaluate our method on a subset of the
Hyper-Kvasir annotated by our clinical collaborators, resulting in SOTA
results, with a recall of 0.956, precision of 0.875, $F_1$ score of 0.914, AP
of 0.952, and can run in real time.