Load safety assessment and compliance is an essential step in the corporate process of every logistics service provider. In 2020, a total of 11,371 police checks of trucks were carried out, during which 9.6% (1091) violations against the load safety regulations were detected. For a logistic service provider, every load safety violation results in height fines and damage to reputation. An assessment of load safety supported by artificial intelligence (AI) will reduce the risk of accidents by unsecured loads and fines during safety assessments. This work shows how photos of the load, taken by the truck driver or the loadmaster after the loading process, can be used to assess load safety. By a trained two-stage artificial neural network (ANN), these photos are classified into three different classes I) cargo loaded safely, II) cargo loaded unsafely, and III) unusable image. By applying several architectures of convolutional neural networks (CNN), it can be shown that it is possible to distinguish between unusable and usable images for cargo safety assessment. This distinction is quite crucial since the truck driver and the loadmaster sometimes provide photos without the essential image features like the case structure of the truck and the whole cargo. A human operator or another ANN will then assess the load safety within the second stage.

翻译：货物装载安全性评估与合规是物流服务提供商企业流程中的关键环节。2020年，共进行了11371次卡车警察检查，其中9.6%（1091次）发现违反货物装载安全规定。对于物流服务提供商而言，每次违反货物装载安全规定都会导致高额罚款和声誉受损。基于人工智能（AI）的货物装载安全性评估将降低因未固定货物导致的事故风险以及安全检查中的罚款风险。本研究展示了如何利用卡车司机或装载主管在装载过程后拍摄的货物照片来评估装载安全性。通过训练两级人工神经网络（ANN），这些照片被分为三类：I) 货物安全装载，II) 货物不安全装载，III) 不可用图像。通过应用多种卷积神经网络（CNN）架构，可以证明区分可用于货物安全评估的不可用图像与可用图像是可行的。这一区分至关重要，因为卡车司机和装载主管有时会提供缺少关键图像特征（如卡车车厢结构和全部货物）的照片。在第二阶段，人工操作员或另一个人工神经网络将评估装载安全性。