Spatial position recognition method of semi-transparent and flexible workpieces: A machine vision based on red light assisted
In the automatic sorting process, overlapping translucent and flexible workpieces on the conveyor belt, blurring the imaging edge features of translucent and flexible workpieces is a challenge to locate the upper and lower workpieces spatially, we propose a method for locating translucent and flexib...
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Veröffentlicht in: | Advances in production engineering & management 2023-03, Vol.18 (1), p.49-65 |
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creator | Bi, Q.L. Lai, M.L. Chen, K. Liu, J.M. Tang, H.L. Teng, X.B. Guo, Y.Y. |
description | In the automatic sorting process, overlapping translucent and flexible workpieces on the conveyor belt, blurring the imaging edge features of translucent and flexible workpieces is a challenge to locate the upper and lower workpieces spatially, we propose a method for locating translucent and flexible workpieces spatially under the overlapping environment in conjunction with the most common automatic sorting of translucent and flexible workpieces such as infusion tube drip buckets. Firstly, we propose a rectangular surface light source based on 650 nm band and monocular CCD for imaging translucent workpieces such as infusion tube drip buckets and optimize the imaging parameters. Secondly, we study a feature matching recognition algorithm for flexible workpieces that are prone to deformation, construct a mapping relationship between the position of overlapping layers and imaging quality of translucent and flexible workpieces such as infusion tube drip buckets based on clarity and information entropy, and establish The mapping relationship between the position of the overlapping layers and the imaging quality of translucent and flexible workpieces such as infusion tube drip buckets is constructed based on clarity and information entropy, and a local spatial coordinate conversion model is established. Finally, the spatial positioning coordinates of overlapping and non-overlapping translucent and flexible workpieces in the local coordinate system are identified, and the results show that the imaging method and theory can be effectively applied to the identification of overlapping and spatial positioning coordinates in the automatic sorting of translucent workpieces such as infusion tube drip buckets. |
doi_str_mv | 10.14743/apem2023.1.456 |
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Firstly, we propose a rectangular surface light source based on 650 nm band and monocular CCD for imaging translucent workpieces such as infusion tube drip buckets and optimize the imaging parameters. Secondly, we study a feature matching recognition algorithm for flexible workpieces that are prone to deformation, construct a mapping relationship between the position of overlapping layers and imaging quality of translucent and flexible workpieces such as infusion tube drip buckets based on clarity and information entropy, and establish The mapping relationship between the position of the overlapping layers and the imaging quality of translucent and flexible workpieces such as infusion tube drip buckets is constructed based on clarity and information entropy, and a local spatial coordinate conversion model is established. Finally, the spatial positioning coordinates of overlapping and non-overlapping translucent and flexible workpieces in the local coordinate system are identified, and the results show that the imaging method and theory can be effectively applied to the identification of overlapping and spatial positioning coordinates in the automatic sorting of translucent workpieces such as infusion tube drip buckets.</description><identifier>ISSN: 1854-6250</identifier><identifier>EISSN: 1855-6531</identifier><identifier>DOI: 10.14743/apem2023.1.456</identifier><language>eng</language><publisher>Maribor: University of Maribor, Faculty of Mechanical Engineering, Production Engineering Institute</publisher><subject>Algorithms ; Automation ; Belt conveyors ; Blurring ; Buckets ; Clarity ; Coordinates ; Deep learning ; Deformation ; Entropy (Information theory) ; Feature recognition ; Imaging ; Light sources ; Machine vision ; Mapping ; Resource recovery ; Vision systems ; Workpieces</subject><ispartof>Advances in production engineering & management, 2023-03, Vol.18 (1), p.49-65</ispartof><rights>Copyright University of Maribor, Faculty of Mechanical Engineering, Production Engineering Institute Mar 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Bi, Q.L.</creatorcontrib><creatorcontrib>Lai, M.L.</creatorcontrib><creatorcontrib>Chen, K.</creatorcontrib><creatorcontrib>Liu, J.M.</creatorcontrib><creatorcontrib>Tang, H.L.</creatorcontrib><creatorcontrib>Teng, X.B.</creatorcontrib><creatorcontrib>Guo, Y.Y.</creatorcontrib><title>Spatial position recognition method of semi-transparent and flexible workpieces: A machine vision based on red light assisted</title><title>Advances in production engineering & management</title><description>In the automatic sorting process, overlapping translucent and flexible workpieces on the conveyor belt, blurring the imaging edge features of translucent and flexible workpieces is a challenge to locate the upper and lower workpieces spatially, we propose a method for locating translucent and flexible workpieces spatially under the overlapping environment in conjunction with the most common automatic sorting of translucent and flexible workpieces such as infusion tube drip buckets. Firstly, we propose a rectangular surface light source based on 650 nm band and monocular CCD for imaging translucent workpieces such as infusion tube drip buckets and optimize the imaging parameters. Secondly, we study a feature matching recognition algorithm for flexible workpieces that are prone to deformation, construct a mapping relationship between the position of overlapping layers and imaging quality of translucent and flexible workpieces such as infusion tube drip buckets based on clarity and information entropy, and establish The mapping relationship between the position of the overlapping layers and the imaging quality of translucent and flexible workpieces such as infusion tube drip buckets is constructed based on clarity and information entropy, and a local spatial coordinate conversion model is established. Finally, the spatial positioning coordinates of overlapping and non-overlapping translucent and flexible workpieces in the local coordinate system are identified, and the results show that the imaging method and theory can be effectively applied to the identification of overlapping and spatial positioning coordinates in the automatic sorting of translucent workpieces such as infusion tube drip buckets.</description><subject>Algorithms</subject><subject>Automation</subject><subject>Belt conveyors</subject><subject>Blurring</subject><subject>Buckets</subject><subject>Clarity</subject><subject>Coordinates</subject><subject>Deep learning</subject><subject>Deformation</subject><subject>Entropy (Information theory)</subject><subject>Feature recognition</subject><subject>Imaging</subject><subject>Light sources</subject><subject>Machine vision</subject><subject>Mapping</subject><subject>Resource recovery</subject><subject>Vision systems</subject><subject>Workpieces</subject><issn>1854-6250</issn><issn>1855-6531</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNo1kElPwzAQhS0EElXpmaslzmm9xUm4VRWbVIkDvVuOM2kNSRxsl-XAf8dt4TRPozdvZj6ErimZU1EIvtAj9IwwPqdzkcszNKFlnmcy5_T8qEUmWU4u0SwEWxOR-qLibIJ-XkYdre7w6IKN1g3Yg3Hb4aR7iDvXYNfiAL3NotdDGLWHIWI9NLjt4MvWHeBP599GCwbCLV7iXpudHQB_2HAIqXWAlHFIbnBnt7s0nK4IEZordNHqLsDsr07R5v5us3rM1s8PT6vlOjNMipjlQE1FhCFFQxgQxrio0ksNLesir4wQlEsoZd6UgkhiuCwIK2tgvCoN4TWfoptT7Ojd-x5CVK9u74e0UbEy4asqwnlyLU4u410IHlo1ettr_60oUUfK6p-yoipR5r-3g3D8</recordid><startdate>20230301</startdate><enddate>20230301</enddate><creator>Bi, Q.L.</creator><creator>Lai, M.L.</creator><creator>Chen, K.</creator><creator>Liu, J.M.</creator><creator>Tang, H.L.</creator><creator>Teng, X.B.</creator><creator>Guo, Y.Y.</creator><general>University of Maribor, Faculty of Mechanical Engineering, Production Engineering Institute</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TA</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BYOGL</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>L6V</scope><scope>M7S</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope></search><sort><creationdate>20230301</creationdate><title>Spatial position recognition method of semi-transparent and flexible workpieces: A machine vision based on red light assisted</title><author>Bi, Q.L. ; Lai, M.L. ; Chen, K. ; Liu, J.M. ; Tang, H.L. ; Teng, X.B. ; Guo, Y.Y.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c264t-5e1c904c07d02e022349854d18b759c44136e865d84060c367028be2398c03b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Automation</topic><topic>Belt conveyors</topic><topic>Blurring</topic><topic>Buckets</topic><topic>Clarity</topic><topic>Coordinates</topic><topic>Deep learning</topic><topic>Deformation</topic><topic>Entropy (Information theory)</topic><topic>Feature recognition</topic><topic>Imaging</topic><topic>Light sources</topic><topic>Machine vision</topic><topic>Mapping</topic><topic>Resource recovery</topic><topic>Vision systems</topic><topic>Workpieces</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bi, Q.L.</creatorcontrib><creatorcontrib>Lai, M.L.</creatorcontrib><creatorcontrib>Chen, K.</creatorcontrib><creatorcontrib>Liu, J.M.</creatorcontrib><creatorcontrib>Tang, H.L.</creatorcontrib><creatorcontrib>Teng, X.B.</creatorcontrib><creatorcontrib>Guo, Y.Y.</creatorcontrib><collection>CrossRef</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>East Europe, Central Europe Database</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><jtitle>Advances in production engineering & management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bi, Q.L.</au><au>Lai, M.L.</au><au>Chen, K.</au><au>Liu, J.M.</au><au>Tang, H.L.</au><au>Teng, X.B.</au><au>Guo, Y.Y.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Spatial position recognition method of semi-transparent and flexible workpieces: A machine vision based on red light assisted</atitle><jtitle>Advances in production engineering & management</jtitle><date>2023-03-01</date><risdate>2023</risdate><volume>18</volume><issue>1</issue><spage>49</spage><epage>65</epage><pages>49-65</pages><issn>1854-6250</issn><eissn>1855-6531</eissn><abstract>In the automatic sorting process, overlapping translucent and flexible workpieces on the conveyor belt, blurring the imaging edge features of translucent and flexible workpieces is a challenge to locate the upper and lower workpieces spatially, we propose a method for locating translucent and flexible workpieces spatially under the overlapping environment in conjunction with the most common automatic sorting of translucent and flexible workpieces such as infusion tube drip buckets. Firstly, we propose a rectangular surface light source based on 650 nm band and monocular CCD for imaging translucent workpieces such as infusion tube drip buckets and optimize the imaging parameters. Secondly, we study a feature matching recognition algorithm for flexible workpieces that are prone to deformation, construct a mapping relationship between the position of overlapping layers and imaging quality of translucent and flexible workpieces such as infusion tube drip buckets based on clarity and information entropy, and establish The mapping relationship between the position of the overlapping layers and the imaging quality of translucent and flexible workpieces such as infusion tube drip buckets is constructed based on clarity and information entropy, and a local spatial coordinate conversion model is established. Finally, the spatial positioning coordinates of overlapping and non-overlapping translucent and flexible workpieces in the local coordinate system are identified, and the results show that the imaging method and theory can be effectively applied to the identification of overlapping and spatial positioning coordinates in the automatic sorting of translucent workpieces such as infusion tube drip buckets.</abstract><cop>Maribor</cop><pub>University of Maribor, Faculty of Mechanical Engineering, Production Engineering Institute</pub><doi>10.14743/apem2023.1.456</doi><tpages>17</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Automation Belt conveyors Blurring Buckets Clarity Coordinates Deep learning Deformation Entropy (Information theory) Feature recognition Imaging Light sources Machine vision Mapping Resource recovery Vision systems Workpieces |
title | Spatial position recognition method of semi-transparent and flexible workpieces: A machine vision based on red light assisted |
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