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For this reason, this paper introduces a deep-learning-based human\u2013robot\u2013safety framework (HRSF) that aims at a dynamical adaptation of robot velocities depending on the separation distance between human and robot while respecting maximum biomechanical force and pressure limits. The applicability of the framework was investigated for four different deep learning approaches that can be used for human body extraction: human body recognition, human body segmentation, human pose estimation, and human body part segmentation. Unlike conventional industrial safety systems, the proposed HRSF differentiates individual human body parts from other objects, enabling optimized robot process execution. Experiments demonstrated a quantitative reduction in cycle time of up to 15% compared to conventional safety technology.<\/jats:p>","DOI":"10.3390\/s25237136","type":"journal-article","created":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T14:59:04Z","timestamp":1763996344000},"page":"7136","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Analysis of Deep-Learning Methods in an ISO\/TS 15066\u2013Compliant Human\u2013Robot Safety Framework"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8335-2874","authenticated-orcid":false,"given":"David","family":"Bricher","sequence":"first","affiliation":[{"name":"Institute of Robotics, Johannes Kepler University, 4040 Linz, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5033-340X","authenticated-orcid":false,"given":"Andreas","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"Institute of Robotics, Johannes Kepler University, 4040 Linz, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1888","DOI":"10.1109\/TCYB.2019.2947532","article-title":"Physical Human\u2013Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators","volume":"51","author":"Ogenyi","year":"2021","journal-title":"IEEE Trans. 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