For this purpose, many test images were manually annotated. Subsequently, an existing machine learning model was further trained with these images (transfer learning). After several iteration stages, it can now already recognize the learned objects very well. The method presented in this talk is also interesting for our counting app. Because here, too, we can use Transfer Learning to recognize several objects of the same type (pipes, tree trunks, racks, vehicles, …) on one image and count them automatically. In this way, we can capture large quantities of objects automatically and in a matter of seconds, and in turn make sense of the number for downstream processes such as loading.