Image based classification of shipments using transfer learning
Markus Leppioja, Pasi Luukka, Christoph Lohrmann
Citation: Recent Advances in Business Analytics. Selected papers of the 2021 KNOWCON-NSAIS workshop on Business Analytics, Jan Stoklasa, Pasi Luukka and Maria Ganzha (eds). ACSIS, Vol. 29, pages 37–44 (2021)
Abstract. This paper focuses on recognizing different postalshipment types from images taken by the sorting machine.Greyscale images obtained from sorting machines are used tobuild a classifier using transfer learning to recognize sevendifferent classes of shipments. Three convolutional neuralnetworks (VGG16, GoogLeNet and ResNet50), that werepretrained using the ImageNet dataset, were used as featureextractors and the extracted features were subsequentlysupplied to a neural network classifier. VGG16 demonstratedthe best performance for six out of the seven classes and achievedan overall mean accuracy of 95.69\% on the independent test set.The model accomplished F1 scores exceeding 90\% for five out ofseven classes, only having a lower recall for the aggregated class``Other'' and shipments from abroad. The results of this studyhighlight the potential of transfer learning for computer visionin the context of shipment classification.
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