  Computer-aided detection, localisation, and segmentation methods can help
improve colonoscopy procedures. Even though many methods have been built to
tackle automatic detection and segmentation of polyps, benchmarking of
state-of-the-art methods still remains an open problem. This is due to the
increasing number of researched computer vision methods that can be applied to
polyp datasets. Benchmarking of novel methods can provide a direction to the
development of automated polyp detection and segmentation tasks. Furthermore,
it ensures that the produced results in the community are reproducible and
provide a fair comparison of developed methods. In this paper, we benchmark
several recent state-of-the-art methods using Kvasir-SEG, an open-access
dataset of colonoscopy images for polyp detection, localisation, and
segmentation evaluating both method accuracy and speed. Whilst, most methods in
literature have competitive performance over accuracy, we show that the
proposed ColonSegNet achieved a better trade-off between an average precision
of 0.8000 and mean IoU of 0.8100, and the fastest speed of 180 frames per
second for the detection and localisation task. Likewise, the proposed
ColonSegNet achieved a competitive dice coefficient of 0.8206 and the best
average speed of 182.38 frames per second for the segmentation task. Our
comprehensive comparison with various state-of-the-art methods reveals the
importance of benchmarking the deep learning methods for automated real-time
polyp identification and delineations that can potentially transform current
clinical practices and minimise miss-detection rates.

Computer Vision and Pattern Recognition