Shipping boxes feature extraction on conveyor belts using real-time object detection systems

The use of real-time object detection systems has significantly increased in the previous decade, ranging from personal open-source projects to industry solutions, as is the case with autonomous vehicles. New state-of-the-art models surface on a yearly basis, if not multiple times a year, and stand as a testament to the velocity of the field's rapid advancement. This paper will focus on the use of such a model on objects such as shipping boxes on conveyor belts used in warehouses to improve logistics. The research is based on the YOLO (You Only Look Once) object detection system with low-quality image input. The model can annotate where on the conveyor belt the objects are and give information on features of the objects such as their size, color, and shape. The images have been extracted from surveillance cameras inside shipping warehouses. Significant results with real-time results have been obtained from video cameras that have high-noise frames, which means that better camera placement and higher quality images can only improve existing results and model performance.