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Visual Detection of Personal Protection Equipment

Visual Detection of Personal Protection Equipment

Through their wide-ranging applications in the manufacturing environments, it is increasingly evident that Artificial Intelligence techniques can increase process reliability and efficiency. Expanding the applications areas of AI in the manufacturing environments, in the EFPF project a specific use-case investigates the use of AI-based visual analytic techniques to ensure operational health and safety compliance. In this use case, a tool for automatic detection of facemasks has been implemented using AI-based visual analytic techniques. The tool orchestrates various systems in the spray booth area of the shop floor to ensure operational safety, compliance with relevant procedures as well as efficiency in the painting process.


The EFPF solution detects that the operator is wearing the correct face masks, checks that the required air temperature is available and opens the spray value automatically. At the same time, the extraction system is turned on and the safety lights indicate the status. The solution utilises a small computer that hosts the EFPF Factory Connector, which is responsible for establishing the connection between various hardware and software components. The AI visual analytic algorithms are tuned to process real-time video feeds. Advance machine learning techniques were used to train the models that are able to recognise compliance with relevant safety procedures (e.g. use of correct mask, the positioning of mask etc) or raise alarms if anomalies or non-compliance are detected. The Factory Connector also provides remote access and connection to the EFPF Data Spine so that the relevant data can be directly analysed to provide valuable intelligence.

Through this visual analytic tool, the painting process was optimised in terms of process reliability and operational safety. The tool provides another building block towards increased digitalisation in the manufacturing sector.
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Web resources: https://www.efpf.org/
Country: DE
Address: Holzmuehlenstr. 84-86, Hamburg 22041
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This items serves as a filter in support of selecting the case and demonstrators associated to the Digital Transformation Pathway Cases Catalogue (see ConnectedFactories Coordination and Support Action - Information sharing and analysis)