Classification of Towels in a Robotic Workcell Using Deep Neural Networks

The industrial laundry industry is becoming increasingly more automated. Inwatec, a company specializing in this field, is developing a new robot (BLIZZ) to automate the process of grasping individual clean towels from a pile, and hand them over to an external folding machine. However, to ensure that towels are folded consistently, information about the type and faces of the towels is required. This paper presents a proof of concept for a towel type and towel face classification system integrated in BLIZZ. These two classification problems are solved by means of a Deep Neural Network (DNN). The performance of the proposed DNN on each of the two classification problems is presented, along with the performance of it solving both classification problems at the same time. It is concluded that the proposed network achieves classification accuracies of 94 .48%, 97.71% and 98.52% on the face classification problem for three different towel types with non-identical faces. On the type classif ication problem, it achieves an accuracy of 99.10% on the full dataset. Additionally, it is concluded that the system achieves an accuracy of 96.96% when simultaneously classifying the type and face of a towel on the full dataset.