{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T22:56:53Z","timestamp":1774997813006,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T00:00:00Z","timestamp":1632268800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The automatic detection of the thread roll\u2019s margin is one of the kernel problems in the textile field. As the traditional detection method based on the thread\u2019s tension has the disadvantages of high cost and low reliability, this paper proposes a technology that installs a camera on a mobile robot and uses computer vision to detect the thread roll\u2018s margin. Before starting, we define a thread roll\u2018s margin as follows: The difference between the thread roll\u2018s radius and the bobbin\u2019s radius. Firstly, we capture images of the thread roll\u2018s end surface. Secondly, we obtain the bobbin\u2019s image coordinates by calculating the image\u2019s convolutions with a Circle Gradient Operator. Thirdly, we fit the thread roll and bobbin\u2019s contours into ellipses, and then delete false detections according to the bobbin\u2019s image coordinates. Finally, we restore every sub-image of the thread roll by a perspective transformation method, and establish the conversion relationship between the actual size and pixel size. The difference value of the two concentric circles\u2019 radii is the thread roll\u2019s margin. However, there are false detections and these errors may be more than 19.4 mm when the margin is small. In order to improve the precision and delete false detections, we use deep learning to detect thread roll and bobbin\u2019s radii and then can calculate the thread roll\u2019s margin. After that, we fuse the two results. However, the deep learning method also has some false detections. As such, in order to eliminate the false detections completely, we estimate the thread roll\u2018s margin according to thread consumption speed. Lastly, we use a Kalman Filter to fuse the measured value and estimated value; the average error is less than 5.7 mm.<\/jats:p>","DOI":"10.3390\/s21196331","type":"journal-article","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T22:50:48Z","timestamp":1632351048000},"page":"6331","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["The Detection of Thread Roll\u2019s Margin Based on Computer Vision"],"prefix":"10.3390","volume":"21","author":[{"given":"Zhiwei","family":"Shi","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weimin","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junru","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1080\/17538963.2010.511905","article-title":"The rise of labor cost and the fall of labor input: Has China reached Lewis turning point","volume":"3","author":"Meiyan","year":"2010","journal-title":"China Econ. 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