  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.
