  Today, more and more, it is necessary that most applications and documents
developed in previous or current technologies to be accessible online on
cloud-based infrastructures. That is why the migration of legacy systems
including their hosts of documents to new technologies and online
infrastructures, using modern Artificial Intelligence techniques, is absolutely
necessary. With the advancement of Artificial Intelligence and Deep Learning
with its multitude of applications, a new area of research is emerging - that
of automated systems development and maintenance. The underlying work objective
that led to this paper aims to research and develop truly intelligent systems
able to analyze user interfaces from various sources and generate real and
usable inferences ranging from architecture analysis to actual code generation.
One key element of such systems is that of artificial scene detection and
analysis based on deep learning computer vision systems. Computer vision models
and particularly deep directed acyclic graphs based on convolutional modules
are generally constructed and trained based on natural images datasets. Due to
this fact, the models will develop during the training process natural image
feature detectors apart from the base graph modules that will learn basic
primitive features. In the current paper, we will present the base principles
of a deep neural pipeline for computer vision applied to artificial scenes
(scenes generated by user interfaces or similar). Finally, we will present the
conclusions based on experimental development and benchmarking against
state-of-the-art transfer-learning implemented deep vision models.
