Image analysis is an essential process in logistics that is mainly based on computer-aided methods. It is used to identify articles without labeling and to precisely determine the position of goods and loading aids in the area (2-D) or in space (3-D). CCD (charge-coupled device) sensors are generally used to capture images for analysis, but increasingly CMOS (complementary metal-oxide-semiconductor) sensors are also being used, either as line sensors with a relative movement on the conveyor line or as 2-D sensors.

In addition to item identification, image analysis systems provide additional information such as the center of gravity of an object or the position of a specific feature. An outstanding area of application for image analysis is plain text recognition, also known as Optical Character Recognition (OCR). Modern OCR systems are able, for example, to recognize address labels on parcels at high reading rates and at high conveyor speeds during throughput.

The advantages of image analysis in logistics are manifold. The automatic identification of items without labels significantly increases efficiency in warehouse management and order picking. The exact determination of the position of goods enables optimized use of the available storage space and efficient loading of means of transport.

However, the integration of image analysis technologies into logistics processes requires careful planning and implementation. This includes the selection of suitable sensors, the development of customized software solutions and the training of personnel in the use of the technology.

Overall, image analysis in logistics offers enormous potential for process optimization and increased efficiency along the entire supply chain. Through the precise identification and localization of goods, it makes a significant contribution to improving operational processes and reducing costs.