  In this paper we extensively explore the suitability of YOLO architectures to
monitor the process flow across a Fischertechnik industry 4.0 application.
Specifically, different YOLO architectures in terms of size and complexity
design along with different prior-shapes assignment strategies are adopted. To
simulate the real world factory environment, we prepared a rich dataset
augmented with different distortions that highly enhance and in some cases
degrade our image qualities. The degradation is performed to account for
environmental variations and enhancements opt to compensate the color
correlations that we face while preparing our dataset. The analysis of our
conducted experiments shows the effectiveness of the presented approach
evaluated using different measures along with the training and validation
strategies that we tailored to tackle the unavoidable color correlations that
the problem at hand inherits by nature.
