This talk introduces a new method for object detection for consumer goods and other applications based on measuring an object’s illumination invariant fluorescent chroma. Chroma is a measure of the colorfulness of an object relative to a similarly illuminated white object. Reflective chroma changes with the illumination, making it a poor choice for object detection in different environments. Fluorescent chroma, however, is nearly illumination invariant, making it an ideal input for object detection algorithms if the fluoresced light can be separated from the reflected light. Simple designs of illuminators, cameras, and filters to achieve that separation are described, and the classification accuracy of the system is shown to be 95-100% under different lighting conditions using the nearest neighbors algorithm with neighborhood component analysis. BASF has designed hundreds of unique colors and coatings for consumer goods and other items that can be used in this application and the system has many advantages compared to CNN object detection systems, as it does not require extensive training sets or an unoccluded view of the object.