  There has been a substantial amount of research involving computer methods
and technology for the detection and recognition of diabetic foot ulcers
(DFUs), but there is a lack of systematic comparisons of state-of-the-art deep
learning object detection frameworks applied to this problem. DFUC2020 provided
participants with a comprehensive dataset consisting of 2,000 images for
training and 2,000 images for testing. This paper summarises the results of
DFUC2020 by comparing the deep learning-based algorithms proposed by the
winning teams: Faster R-CNN, three variants of Faster R-CNN and an ensemble
method; YOLOv3; YOLOv5; EfficientDet; and a new Cascade Attention Network. For
each deep learning method, we provide a detailed description of model
architecture, parameter settings for training and additional stages including
pre-processing, data augmentation and post-processing. We provide a
comprehensive evaluation for each method. All the methods required a data
augmentation stage to increase the number of images available for training and
a post-processing stage to remove false positives. The best performance was
obtained from Deformable Convolution, a variant of Faster R-CNN, with a mean
average precision (mAP) of 0.6940 and an F1-Score of 0.7434. Finally, we
demonstrate that the ensemble method based on different deep learning methods
can enhanced the F1-Score but not the mAP.
