[{"publication_status":"published","has_accepted_license":"1","keyword":["Computer Vision","Architecture","Building Construction","Robotics in Construction"],"publisher":"International Association for Automation and Robotics in Construction (IAARC)","_id":"14036","publication":"Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)","oa":"1","file":[{"success":1,"relation":"main_file","file_id":"14039","file_size":4708066,"file_name":"ISARC_Robotic_Vision_Yusuf_Aykin.pdf","content_type":"application/pdf","access_level":"open_access","date_created":"2026-09-17T14:43:28Z","creator":"fpq-3n9","date_updated":"2026-09-17T14:43:28Z"}],"date_updated":"2026-09-17T14:43:42Z","editor":[{"id":"64589","last_name":"Sachs","full_name":"Sachs, Hans","first_name":"Hans"}],"author":[{"full_name":"Aykin, Yusuf","first_name":"Yusuf","id":"84542","last_name":"Aykin"},{"id":"64589","last_name":"Sachs","first_name":"Hans","full_name":"Sachs, Hans"},{"full_name":"Gerzen, Nikolai","first_name":"Nikolai","id":"76704","last_name":"Gerzen"}],"quality_controlled":"1","doi":"10.22260/isarc2025/0026","publication_identifier":{"issn":["2413-5844"]},"file_date_updated":"2026-09-17T14:43:28Z","year":"2025","citation":{"havard":"Y. Aykin, H. Sachs, N. Gerzen, Enhancing Robotic Vision through Deep Learning Techniques: From Detection to Construction, International Association for Automation and Robotics in Construction (IAARC), 2025.","van":"Aykin Y, Sachs H, Gerzen N. Enhancing Robotic Vision through Deep Learning Techniques: From Detection to Construction. Sachs H, editor. Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC). International Association for Automation and Robotics in Construction (IAARC); 2025.","short":"Y. Aykin, H. Sachs, N. Gerzen, Enhancing Robotic Vision through Deep Learning Techniques: From Detection to Construction, International Association for Automation and Robotics in Construction (IAARC), 2025.","bjps":"<b>Aykin Y, Sachs H and Gerzen N</b> (2025) <i>Enhancing Robotic Vision through Deep Learning Techniques: From Detection to Construction</i>, Sachs H (ed.). International Association for Automation and Robotics in Construction (IAARC).","apa":"Aykin, Y., Sachs, H., &#38; Gerzen, N. (2025). Enhancing Robotic Vision through Deep Learning Techniques: From Detection to Construction. In H. Sachs (Ed.), <i>Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)</i>. International Association for Automation and Robotics in Construction (IAARC). <a href=\"https://doi.org/10.22260/isarc2025/0026\">https://doi.org/10.22260/isarc2025/0026</a>","chicago":"Aykin, Yusuf, Hans Sachs, and Nikolai Gerzen. <i>Enhancing Robotic Vision through Deep Learning Techniques: From Detection to Construction</i>. Edited by Hans Sachs. <i>Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)</i>. International Association for Automation and Robotics in Construction (IAARC), 2025. <a href=\"https://doi.org/10.22260/isarc2025/0026\">https://doi.org/10.22260/isarc2025/0026</a>.","chicago-de":"Aykin, Yusuf, Hans Sachs und Nikolai Gerzen. 2025. <i>Enhancing Robotic Vision through Deep Learning Techniques: From Detection to Construction</i>. Hg. von Hans Sachs. <i>Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)</i>. International Association for Automation and Robotics in Construction (IAARC). doi:<a href=\"https://doi.org/10.22260/isarc2025/0026\">10.22260/isarc2025/0026</a>, .","ufg":"<b>Aykin, Yusuf/Sachs, Hans/Gerzen, Nikolai</b>: Enhancing Robotic Vision through Deep Learning Techniques: From Detection to Construction, hg. von Sachs, Hans, o. O. 2025.","ieee":"Y. Aykin, H. Sachs, and N. Gerzen, <i>Enhancing Robotic Vision through Deep Learning Techniques: From Detection to Construction</i>. International Association for Automation and Robotics in Construction (IAARC), 2025. doi: <a href=\"https://doi.org/10.22260/isarc2025/0026\">10.22260/isarc2025/0026</a>.","ama":"Aykin Y, Sachs H, Gerzen N. <i>Enhancing Robotic Vision through Deep Learning Techniques: From Detection to Construction</i>. (Sachs H, ed.). International Association for Automation and Robotics in Construction (IAARC); 2025. doi:<a href=\"https://doi.org/10.22260/isarc2025/0026\">10.22260/isarc2025/0026</a>","din1505-2-1":"<span style=\"font-variant:small-caps;\">Aykin, Yusuf</span> ; <span style=\"font-variant:small-caps;\">Sachs, Hans</span> ; <span style=\"font-variant:small-caps;\">Gerzen, Nikolai</span> ; <span style=\"font-variant:small-caps;\">Sachs, H.</span> (Hrsg.): <i>Enhancing Robotic Vision through Deep Learning Techniques: From Detection to Construction</i> : International Association for Automation and Robotics in Construction (IAARC), 2025","mla":"Aykin, Yusuf, et al. “Enhancing Robotic Vision through Deep Learning Techniques: From Detection to Construction.” <i>Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)</i>, edited by Hans Sachs, International Association for Automation and Robotics in Construction (IAARC), 2025, <a href=\"https://doi.org/10.22260/isarc2025/0026\">https://doi.org/10.22260/isarc2025/0026</a>."},"title":"Enhancing Robotic Vision through Deep Learning Techniques: From Detection to Construction","ddc":["600"],"status":"public","date_created":"2026-09-17T14:27:18Z","language":[{"iso":"eng"}],"department":[{"_id":"DEP1055"}],"user_id":"64589","abstract":[{"text":"This paper presents a robotic system developed to enhance\r\nautomation in construction workflows through advanced AI\r\nand computer vision technologies. The system integrates\r\na robotic arm with a 3D point cloud camera and state-\r\nof-the-art 2D pre-trained Deep Learning models, such as\r\nGroundingDINO and SegmentAnything, to detect and seg-\r\nment construction elements in 3D dynamic, unstructured\r\nenvironments. By processing point cloud data from the cam-\r\nera and aligning it with real-world coordinates, the system\r\nachieves precise object localization, enabling tasks such as\r\nelement handling and assembly. Designed to address chal-\r\nlenges like clutter, occlusion, and variability in construction\r\nsites, this system bridges the gap between controlled labora-\r\ntory conditions and real-world applications. Experimental\r\nevaluations highlight its potential to improve efficiency and\r\nadaptability in construction tasks.","lang":"eng"}],"type":"conference_editor_article"}]