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.

利用单个图像，我们的算法可以自动识别物流运输单元的特征、数量和排列方式，并以大约 85％的测试用例正确识别包装结构，特别是最常见的包装类型，其基础是深度学习模型、卷积神经网络和计算机视觉方法。