To better retain the deep features of an image and solve the sparsity problem of the end-to-
end segmentation model, we propose a new deep convolutional network model for medical …

In the realm of solar photovoltaic system image segmentation, existing deep learning
networks focus almost exclusively on single image sources both in terms of sensors used …

Due to tiny edge and texture details of harmful algae blooms (HABs), existing segmentation
networks are not effective for HABs segmentation. In order to solve the above problems, this …

Generating pixel-level annotations for semantic segmentation tasks of high-resolution
remote sensing images is both time-consuming and labor-intensive, which has led to …

Railway transportation has been integrated into people's lives. According to the “Notice on
the release of the General Technical Specification of High-speed Railway Power Supply …

Prostate cancer remains a prevalent health concern, emphasizing the critical need for early
diagnosis and precise treatment strategies to mitigate mortality rates. The accurate …

Semantic image segmentation based on deep learning is gaining popularity because it is
giving promising results in medical image analysis, automated land categorization, remote …

In cornfields, factors such as the similarity between corn seedlings and weeds and the
blurring of plant edge details pose challenges to corn and weed segmentation. In addition …

With the rapid development of satellite and internet of things (IoT) technology, it becomes
more and more convenient to acquire high-resolution satellite images from the ground …

Land cover (LC) categorization is considered a necessary task of intelligent interpretation
technology for remote sensing imagery that is intended to categorize every pixel to perform …