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Human-machine interaction can be supported by the detection of humans through the simultaneous localization and distinction from non-human objects. This paper compares modern object detection algorithms (Damo-YOLO, YOLOv6, YOLOv7 and YOLOv8) in combination with Transfer Learning and Super Resolution in different scenarios to achieve human detection on low resolution infrared images. The data set created for this purpose includes images of an empty room, images of warm coffee cups, and images of people in various scenarios and at distances ranging from two to six meters. The Average Precision AP@50 and AP@50:95 values achieved across all scenarios reach up to 98.02 % and 66.99 % respectively.
In this paper, we present the main obstacles faced by small and medium-sized enterprises (SMEs) when implementing artificial intelligence (AI), and suggest a novel “plug and play” guided approach for further integration. In order to identify the relevant barriers, we first compile results from recent literature reviews that address challenges specific to SMEs and AI. Then, based on the AI maturity model for SMEs by Schuster et al., we analyze the current status of AI in local German SMEs with which we have worked in the context of the “KI-Labor Südbaden” project. Based on the results of the analysis, we detail a structured approach utilizing pre-identified successful AI implementations as the basis for further technological development. By structuring their AI integration on known successful use cases, SMEs have the chance to leapfrog their AI development and remain competitive in today’s landscape.
In this paper we present the concept of the "KI-Labor Südbaden" to support regional companies in the use of AI technologies. The approach is based on the "Periodic Table of AI" and is extended with both new dimensions for sustainability, and the impact of AI on the working environment. It is illustrated on the basis of three real-world use cases: 1. The detection of humans with lowresolution infrared (IR) images for collaborative robotics; 2. The use of machine data from specifically designed vehicles; 3. State-of-the-art Large Language Models (LLMs) applied to internal company documents. We explain the use cases, thereby demonstrating how to apply the Periodic Table of AI to structure AI applications.