  Creating an object detector, in computer vision, has some common challenges
when initially developed based on Convolutional Neural Network (CNN)
architecture. These challenges are more apparent when creating model that needs
to adapt to images captured by various camera orientations, lighting
conditions, and environmental changes. The availability of the initial training
samples to cover all these conditions can be an enormous challenge with a time
and cost burden. While the problem can exist when creating any type of object
detection, some types are less common and have no pre-labeled image datasets
that exists publicly. Sometime public datasets are not reliable nor
comprehensive for a rare object type. Vehicle wheel is one of those example
that been chosen to demonstrate the approach of creating a lighting and
rotation invariant real-time detector based on YOLOv5 architecture. The
objective is to provide a simple approach that could be used as a reference for
developing other types of real-time object detectors.
