The last decade has marked a rapid and significant growth of the global market of warehouse automation. The biggest challenge lies in the identification and handling of foreign objects. The aim of this research is to investigate whether a usable relation exist between object features such as size or shape, and barcode location, that can be used to robustly identify objects in a bin.

A deep convolutional neural network (CNN) is built in MATLAB and trained on a labeled dataset of thousand product images from various perspectives, to determine on which surface of a product the barcode lies. Training results show that while the training set accuracy reaches 100%, a maximum validation accuracy of only 45% is achieved. A larger dataset is required to reduce overfitting and increase the validation accuracy. When sufficient classification accuracies are reached, smart picking strategies can be implemented to efficiently handle products.