BoT-YOLOv8: A high accuracy and stability initial weld position segmentation method for medium-thickness plate

BoT-YOLOv8: A high accuracy and stability initial weld position segmentation method for medium-thickness plate | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article BoT-YOLOv8: A high accuracy and stability initial weld position segmentation method for medium-thickness plate Zongmin Liu, Jie Li, Shunlong Zhang, Lei Qin, Changcheng Shi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3820453/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Mar, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract To address the technical bottleneck of autonomous vision guidance for the initial weld position of medium-thickness plate in robot welding. This paper proposes a high accuracy and stability initial weld position segmentation method for medium-thickness plate, this method is developed by integrating the Bottleneck Transformer (BoT) into YOLOv8, termed as BoT-YOLOv8. Firstly, aim to filter out redundant information in the image and enhance the model's capability to express features, the BoT is added behind the last bottleneck layer in the residual module of the YOLOv8 neck structure. Subsequently, in order to obtain the multi-scale information of the target, the atrous convolution is incorporated as the spatial pyramid pooling structure to establish connections between the backbone and the neck of this model. Furthermore, to facilitate the learning of weld position characteristics for the welding robot, the Hue-Saturation-Value (HSV) space region segmentation method is utilized to postprocess the weld seam features. Finally, ablation experiments are conducted on the self-created weld dataset. The results demonstrate that the proposed method achieves a trade-off between detection accuracy (93.1% \({mAP}^{0.5}\) ) and detection speed (26.5 \(FPS\) ) on a 12GB NVIDIA GeForce RTX 3060 GPU. In addition, compared with the existing methods, the presented method exhibits stronger anti-interference capability. Robot welding Medium-thickness plate༛Initial weld position segmentation༛YOLOv8༛Bottleneck Transformer Full Text Cite Share Download PDF Status: Published Journal Publication published 18 Mar, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 19 Aug, 2024 Reviewers agreed at journal 07 Jan, 2024 Reviewers invited by journal 04 Jan, 2024 Editor assigned by journal 01 Jan, 2024 First submitted to journal 28 Dec, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3820453","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":265411782,"identity":"81514a29-1eca-44b9-a9e5-0b99e5ca0aa4","order_by":0,"name":"Zongmin 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This paper proposes a high accuracy and stability initial weld position segmentation method for medium-thickness plate, this method is developed by integrating the Bottleneck Transformer (BoT) into YOLOv8, termed as BoT-YOLOv8. Firstly, aim to filter out redundant information in the image and enhance the model's capability to express features, the BoT is added behind the last bottleneck layer in the residual module of the YOLOv8 neck structure. Subsequently, in order to obtain the multi-scale information of the target, the atrous convolution is incorporated as the spatial pyramid pooling structure to establish connections between the backbone and the neck of this model. Furthermore, to facilitate the learning of weld position characteristics for the welding robot, the Hue-Saturation-Value (HSV) space region segmentation method is utilized to postprocess the weld seam features. Finally, ablation experiments are conducted on the self-created weld dataset. The results demonstrate that the proposed method achieves a trade-off between detection accuracy (93.1% \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({mAP}^{0.5}\\)\u003c/span\u003e\u003c/span\u003e) and detection speed (26.5 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(FPS\\)\u003c/span\u003e\u003c/span\u003e) on a 12GB NVIDIA GeForce RTX 3060 GPU. 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