Distance Measurement in Industrial Scenarios
3D reconstruction technology is one of the important technologies of computer vision. Compared with 2D data, 3D space contains more abundant information, including location information, local / global features and so on. It is of great significance to solve the problem of target distance measurement...
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| Veröffentlicht in: | Journal of physics. Conference series 2022-01, Vol.2166 (1), p.12065 |
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| creator | Liu, Hao Liu, Chengzhao Ma, Chenzhe Zheng, Hantao Xu, Jianglong |
| description | 3D reconstruction technology is one of the important technologies of computer vision. Compared with 2D data, 3D space contains more abundant information, including location information, local / global features and so on. It is of great significance to solve the problem of target distance measurement in a single image scene. It is difficult for neural network to predict hole size without image scale information. In this paper, we focus on the measurement of objects with little change in height in the image. We use the relationship between camera parameters and regional features and the internal relationship between regional features to solve the problems of three-dimensional parameter reconstruction and monocular image distance measurement and use the standard cross entropy loss to optimize the transformer model. This model has achieved good results on the sampled data set. |
| doi_str_mv | 10.1088/1742-6596/2166/1/012065 |
| format | Article |
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This model has achieved good results on the sampled data set.</description><subject>Computer vision</subject><subject>Distance measurement</subject><subject>Hole size</subject><subject>Image reconstruction</subject><subject>Mathematical models</subject><subject>Neural networks</subject><subject>Parameters</subject><subject>Physics</subject><issn>1742-6588</issn><issn>1742-6596</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>O3W</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqFUEtLAzEQDqJgrf4GF7y6bh5NdnKUarVQ8aCeQ0xnIaXNrsnuwX9vlpV6dBiYge8xzEfINaN3jAJUrF7wUkmtKs6UqlhFGadKnpDZETk97gDn5CKlHaUiVz0jtw8-9TY4LF7QpiHiAUNf-FCsw3ZIffR2X7w5DDb6Nl2Ss8buE179zjn5WD2-L5_LzevTenm_KR0XWpagUQrkgmvgSnDbYFMjMKeF2wouEEYQlOYaPxcZcVrrhkHdAOW5hZiTm8m3i-3XgKk3u3aIIZ802VCCAi51ZtUTy8U2pYiN6aI_2PhtGDVjNGZ82owBmDEaw8wUTVaKSenb7s_6P9UPTnhjQw</recordid><startdate>20220101</startdate><enddate>20220101</enddate><creator>Liu, Hao</creator><creator>Liu, Chengzhao</creator><creator>Ma, Chenzhe</creator><creator>Zheng, Hantao</creator><creator>Xu, Jianglong</creator><general>IOP Publishing</general><scope>O3W</scope><scope>TSCCA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>H8D</scope><scope>HCIFZ</scope><scope>L7M</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20220101</creationdate><title>Distance Measurement in Industrial Scenarios</title><author>Liu, Hao ; Liu, Chengzhao ; Ma, Chenzhe ; Zheng, Hantao ; Xu, Jianglong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2395-89e53e232982632afef7e81c93cd323e83e2386929eb47e8c999f187f80280233</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Computer vision</topic><topic>Distance measurement</topic><topic>Hole size</topic><topic>Image reconstruction</topic><topic>Mathematical models</topic><topic>Neural networks</topic><topic>Parameters</topic><topic>Physics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Hao</creatorcontrib><creatorcontrib>Liu, Chengzhao</creatorcontrib><creatorcontrib>Ma, Chenzhe</creatorcontrib><creatorcontrib>Zheng, Hantao</creatorcontrib><creatorcontrib>Xu, Jianglong</creatorcontrib><collection>IOP Publishing Free Content</collection><collection>IOPscience (Open Access)</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Aerospace Database</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content 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>ProQuest Central China</collection><jtitle>Journal of physics. 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| subjects | Computer vision Distance measurement Hole size Image reconstruction Mathematical models Neural networks Parameters Physics |
| title | Distance Measurement in Industrial Scenarios |
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