Our findings show that GAN-based techniques and spatial-level transformations are the most promising for improving the learning of deep models on this problem, with the StarGANv2 + F with a probability of 0. 3 achieving the highest F-score value on the Ricord1a dataset in the unified training strategy.
In this work, we propose an extensive analysis of how different data augmentation techniques improve the training of encoder-decoder neural networks on this problem.
To the best of our knowledge, this is the largest evaluation in number of encoders, decoders, and datasets proposed in the field of Covid-19 CT segmentation.
With the rise of automation, unmanned vehicles became a hot topic both as commercial products and as a scientific research topic.
Combining our method with others surpasses traditional techniques such as horizontal-flip in 0. 52% for F-measure and 1. 19% for Precision.
Civilian Unmanned Aerial Vehicles (UAVs) are becoming more accessible for domestic use.
Signal Processing Robotics
For the quantitative analysis, a pre-trained Mask R-CNN was used for the detection and segmentation of birds on Pascal VOC, Caltech-UCSD Birds 200-2011, and our new dataset entitled FakeSet.
However, there is no extensive comparison between the two networks in the SOD literature endorsing the effectiveness of Mask-RCNNs over FCN when segmenting salient objects.