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http://hdl.handle.net/2183/39905 A Cost-Effective Thermal Imaging Safety Sensor for Industry 5.0 and Collaborative Robotics
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Barros, D., Fraga-Lamas, P., Fernández-Caramés, T.M., Lopes, S.I. (2023). A Cost-Effective Thermal Imaging Safety Sensor for Industry 5.0 and Collaborative Robotics. In: Lopes, S.I., Fraga-Lamas, P., Fernández-Caramés, T.M., Dawadi, B.R., Rawat, D.B., Shakya, S. (eds) Smart Technologies for Sustainable and Resilient Ecosystems. Edge-IoT SmartGov 2022 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 510. Springer, Cham. https://doi.org/10.1007/978-3-031-35982-8_1
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[Abstract]: The Industry 5.0 paradigm focuses on industrial operator well-being and sustainable manufacturing practices, where humans play a central role, not only during the repetitive and collaborative tasks of the manufacturing process, but also in the management of the factory floor assets. Human factors, such as ergonomics, safety, and well-being, push the human-centric smart factory to efficiently adopt novel technologies while minimizing environmental and social impact. As operations at the factory floor increasingly rely on collaborative robots (CoBots) and flexible manufacturing systems, there is a growing demand for redundant safety mechanisms (i.e., automatic human detection in the proximity of machinery that is under operation). Fostering enhanced process safety for human proximity detection allows for the protection against possible incidents or accidents with the deployed industrial devices and machinery. This paper introduces the design and implementation of a cost-effective thermal imaging Safety Sensor that can be used in the scope of Industry 5.0 to trigger distinct safe mode states in manufacturing processes that rely on collaborative robotics. The proposed Safety Sensor uses a hybrid detection approach and has been evaluated under controlled environmental conditions. The obtained results show a 97% accuracy at low computational cost when using the developed hybrid method to detect the presence of humans in thermal images.
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Presented at: 3rd International Conference on Intelligent Edge Processing in the IoT Era, Edge-IoT 2022, and the 4th International Conference on Smart Governance for Sustainable Smart Cities, SmartGov 2022 Virtual, Online, 16 November 2022 through 18 November 2022
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-031-35982-8_1
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-031-35982-8_1
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