{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T06:38:23Z","timestamp":1780555103435,"version":"3.54.1"},"reference-count":38,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,9]],"date-time":"2025-08-09T00:00:00Z","timestamp":1754697600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Projects of Scientific and Technological Innovation in Daiyue District, Tai\u2019an City","award":["CXXM\u20132021006"],"award-info":[{"award-number":["CXXM\u20132021006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In welding applications, line-structured-light vision is widely used for seam tracking, but intense noise from arc glow, spatter, smoke, and reflections makes reliable laser-stripe segmentation difficult. To address these challenges, we propose EUFNet, an uncertainty-driven symmetrical two-stage segmentation network for precise stripe extraction under real-world welding conditions. In the first stage, a lightweight backbone generates a coarse stripe mask and a pixel-wise uncertainty map; in the second stage, a functionally mirrored refinement network uses this uncertainty map to symmetrically guide fine-tuning of the same image regions, thereby preserving stripe continuity. We further employ an uncertainty-weighted loss that treats ambiguous pixels and their corresponding evidence in a one-to-one, symmetric manner. Evaluated on a large-scale dataset of 3100 annotated welding images, EUFNet achieves a mean IoU of 89.3% and a mean accuracy of 95.9% at 236.7 FPS (compared to U-Net\u2019s 82.5% mean IoU and 90.2% mean accuracy), significantly outperforming existing approaches in both accuracy and real-time performance. Moreover, EUFNet generalizes effectively to the public WLSD benchmark, surpassing state-of-the-art baselines in both accuracy and speed. These results confirm that a structurally and functionally symmetric, uncertainty-driven two-stage refinement strategy\u2014combined with targeted loss design and efficient feature integration\u2014yields high-precision, real-time performance for automated welding vision.<\/jats:p>","DOI":"10.3390\/sym17081280","type":"journal-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T08:10:32Z","timestamp":1754899832000},"page":"1280","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Laser Stripe Segmentation Network Based on Evidential Uncertainty Theory Modeling Fine-Tuning Optimization Symmetric Algorithm"],"prefix":"10.3390","volume":"17","author":[{"given":"Chenbo","family":"Shi","sequence":"first","affiliation":[{"name":"College of lntelligent Equipment, Shandong University of Science and Technology, Taian 271019, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Delin","family":"Wang","sequence":"additional","affiliation":[{"name":"College of lntelligent Equipment, Shandong University of Science and Technology, Taian 271019, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of lntelligent Equipment, Shandong University of Science and Technology, Taian 271019, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3865-963X","authenticated-orcid":false,"given":"Chun","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of lntelligent Equipment, Shandong University of Science and Technology, Taian 271019, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Yan","sequence":"additional","affiliation":[{"name":"Beijing Botsing Technology Co., Ltd., Beijing 100176, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changsheng","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of lntelligent Equipment, Shandong University of Science and Technology, Taian 271019, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3624-5807","authenticated-orcid":false,"given":"Xiaobing","family":"Feng","sequence":"additional","affiliation":[{"name":"Beijing Botsing Technology Co., Ltd., Beijing 100176, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5065","DOI":"10.1007\/s00170-024-14396-9","article-title":"A review on optimization of autonomous welding parameters for robotics applications","volume":"134","author":"Ali","year":"2024","journal-title":"Int. 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