  Detection of Graphical User Interface (GUI) elements is a crucial task for
automatic code generation from images and sketches, GUI testing, and GUI
search. Recent studies have leveraged both old-fashioned and modern computer
vision (CV) techniques. Oldfashioned methods utilize classic image processing
algorithms (e.g. edge detection and contour detection) and modern methods use
mature deep learning solutions for general object detection tasks. GUI element
detection, however, is a domain-specific case of object detection, in which
objects overlap more often, and are located very close to each other, plus the
number of object classes is considerably lower, yet there are more objects in
the images compared to natural images. Hence, the studies that have been
carried out on comparing various object detection models, might not apply to
GUI element detection. In this study, we evaluate the performance of the four
most recent successful YOLO models for general object detection tasks on GUI
element detection and investigate their accuracy performance in detecting
various GUI elements.
