Fully-Automated Packaging Structure Recognition in Logistics Environments

TL;DR

This paper presents a fully-automated deep learning-based method for recognizing packaging structures in logistics images, significantly reducing manual effort in verifying transported goods.

Contribution

It introduces a novel approach combining CNNs and computer vision for complete packaging recognition from a single image, trained on a custom logistics dataset.

Findings

Achieves 85% accuracy in packaging structure recognition

Reaches 91% accuracy on common package types

Demonstrates effectiveness of deep learning in logistics automation

Abstract

Within a logistics supply chain, a large variety of transported goods need to be handled, recognized and checked at many different network points. Often, huge manual effort is involved in recognizing or verifying packet identity or packaging structure, for instance to check the delivery for completeness. We propose a method for complete automation of packaging structure recognition: Based on a single image, one or multiple transport units are localized and, for each of these transport units, the characteristics, the total number and the arrangement of its packaging units is recognized. Our algorithm is based on deep learning models, more precisely convolutional neural networks for instance segmentation in images, as well as computer vision methods and heuristic components. We use a custom data set of realistic logistics images for training and evaluation of our method. We show that the solution is capable of correctly recognizing the packaging structure in approximately 85% of our test cases, and even more (91%) when focusing on most common package types.