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Creating an object detector, in computer vision, has some common challengeswhen initially developed based on Convolutional Neural Network (CNN)architecture. These challenges are more apparent when creating model that needsto adapt to images captured by various camera orientations, lightingconditions, and environmental changes. The availability of the initial trainingsamples to cover all these conditions can be an enormous challenge with a timeand cost burden. While the problem can exist when creating any type of objectdetection, some types are less common and have no pre-labeled image datasetsthat exists publicly. Sometime public datasets are not reliable norcomprehensive for a rare object type. Vehicle wheel is one of those examplethat been chosen to demonstrate the approach of creating a lighting androtation invariant real-time detector based on YOLOv5 architecture. Theobjective is to provide a simple approach that could be used as a reference fordeveloping other types of real-time object detectors.

Lighting and Rotation Invariant Real-time Vehicle Wheel Detector based on YOLOv5