论文标题
物流环境中的完全自动包装结构识别
Fully-Automated Packaging Structure Recognition in Logistics Environments
论文作者
论文摘要
在物流供应链中，需要在许多不同的网络点处理，认可和检查各种各样的运输商品。通常，巨大的手动努力涉及识别或验证数据包身份或包装结构，例如检查交付的完整性。我们提出了一种完全自动化包装结构识别的方法：基于单个图像，一个或多个传输单元是局部化的，并且对于这些传输单元中的每一个，特征，总数和包装单元的排列都被识别。我们的算法基于深度学习模型，更精确的卷积神经网络，例如图像中的分割以及计算机视觉方法和启发式组件。我们使用定制数据集的现实物流图像来培训和评估我们的方法。我们表明，该解决方案能够在大约85％的测试用例中正确识别包装结构，而当关注大多数常见的包装类型时，则能够正确识别（91％）。
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.
