Assembly Integrity Detection Based on 3D CAD Model Article Swipe
Fanwu Meng
,
Di Wu
,
Tao Gong
,
Xiangyi Xiang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3398803
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3398803
Incomplete assembly could affect product quality and performance, and even cause inestimable losses. In the industrial environment, the lack of image datasets and the inability to obtain the physical assembly in advance is a challenge for machine vision to detect assembly integrity. We propose a 3D CAD model-based method to detect assembly integrity without physical assembly. 3D CAD models are utilized for 2D image rendering and dataset construction, and automatic labeling can be achieved through edge extraction and minimum enclosing rectangle fitting rather than by hand. An end-to-end detection neural network based on Faster-RCNN is trained in the datasets. The VGG network as detection neural network to extract features, and the generated feature map determines the candidate detection region through RPN. Fast-RCNN then detects the object in the image. Finally, by detecting images from multiple views with dimension reduction and comparing them with prior knowledge, we can judge whether there are missing parts in the assembly. Experimental results show that the network model can accurately detect the integrity of the assembly, and the average accuracy (mAP) reaches 88.9%. The method can also provide theoretical guidance for future physical assembly and shorten the experimental process.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3398803
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/10526237.pdf
- OA Status
- gold
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- 10
- OpenAlex ID
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Raw OpenAlex JSON
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https://doi.org/10.1109/access.2024.3398803Digital Object Identifier
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Assembly Integrity Detection Based on 3D CAD ModelWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-01-01Full publication date if available
- Authors
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Fanwu Meng, Di Wu, Tao Gong, Xiangyi XiangList of authors in order
- Landing page
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https://doi.org/10.1109/access.2024.3398803Publisher landing page
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10526237.pdfDirect link to full text PDF
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goldOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10526237.pdfDirect OA link when available
- Concepts
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Computer science, Artificial intelligence, CAD, Rendering (computer graphics), Object detection, Pattern recognition (psychology), Feature extraction, Computer vision, Process (computing), Artificial neural network, Data mining, Engineering drawing, Operating system, EngineeringTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.a | 33, 44 |
| abstract_inverted_index.2D | 62 |
| abstract_inverted_index.3D | 45, 56 |
| abstract_inverted_index.An | 86 |
| abstract_inverted_index.In | 13 |
| abstract_inverted_index.We | 42 |
| abstract_inverted_index.as | 102 |
| abstract_inverted_index.be | 72 |
| abstract_inverted_index.by | 84, 130 |
| abstract_inverted_index.in | 30, 96, 126, 153 |
| abstract_inverted_index.is | 32, 94 |
| abstract_inverted_index.of | 19, 168 |
| abstract_inverted_index.on | 92 |
| abstract_inverted_index.to | 25, 38, 49, 106 |
| abstract_inverted_index.we | 145 |
| abstract_inverted_index.CAD | 46, 57 |
| abstract_inverted_index.The | 99, 178 |
| abstract_inverted_index.VGG | 100 |
| abstract_inverted_index.and | 6, 8, 22, 65, 68, 77, 109, 139, 171, 189 |
| abstract_inverted_index.are | 59, 150 |
| abstract_inverted_index.can | 71, 146, 163, 180 |
| abstract_inverted_index.for | 35, 61, 185 |
| abstract_inverted_index.map | 113 |
| abstract_inverted_index.the | 14, 17, 23, 27, 97, 110, 115, 124, 127, 154, 160, 166, 169, 172, 191 |
| abstract_inverted_index.RPN. | 120 |
| abstract_inverted_index.also | 181 |
| abstract_inverted_index.edge | 75 |
| abstract_inverted_index.even | 9 |
| abstract_inverted_index.from | 133 |
| abstract_inverted_index.lack | 18 |
| abstract_inverted_index.show | 158 |
| abstract_inverted_index.than | 83 |
| abstract_inverted_index.that | 159 |
| abstract_inverted_index.them | 141 |
| abstract_inverted_index.then | 122 |
| abstract_inverted_index.with | 136, 142 |
| abstract_inverted_index.(mAP) | 175 |
| abstract_inverted_index.based | 91 |
| abstract_inverted_index.cause | 10 |
| abstract_inverted_index.could | 2 |
| abstract_inverted_index.hand. | 85 |
| abstract_inverted_index.image | 20, 63 |
| abstract_inverted_index.judge | 147 |
| abstract_inverted_index.model | 162 |
| abstract_inverted_index.parts | 152 |
| abstract_inverted_index.prior | 143 |
| abstract_inverted_index.there | 149 |
| abstract_inverted_index.views | 135 |
| abstract_inverted_index.affect | 3 |
| abstract_inverted_index.detect | 39, 50, 165 |
| abstract_inverted_index.future | 186 |
| abstract_inverted_index.image. | 128 |
| abstract_inverted_index.images | 132 |
| abstract_inverted_index.method | 48, 179 |
| abstract_inverted_index.models | 58 |
| abstract_inverted_index.neural | 89, 104 |
| abstract_inverted_index.object | 125 |
| abstract_inverted_index.obtain | 26 |
| abstract_inverted_index.rather | 82 |
| abstract_inverted_index.region | 118 |
| abstract_inverted_index.vision | 37 |
| abstract_inverted_index.advance | 31 |
| abstract_inverted_index.average | 173 |
| abstract_inverted_index.dataset | 66 |
| abstract_inverted_index.detects | 123 |
| abstract_inverted_index.extract | 107 |
| abstract_inverted_index.feature | 112 |
| abstract_inverted_index.fitting | 81 |
| abstract_inverted_index.losses. | 12 |
| abstract_inverted_index.machine | 36 |
| abstract_inverted_index.minimum | 78 |
| abstract_inverted_index.missing | 151 |
| abstract_inverted_index.network | 90, 101, 105, 161 |
| abstract_inverted_index.product | 4 |
| abstract_inverted_index.propose | 43 |
| abstract_inverted_index.provide | 182 |
| abstract_inverted_index.quality | 5 |
| abstract_inverted_index.reaches | 176 |
| abstract_inverted_index.results | 157 |
| abstract_inverted_index.shorten | 190 |
| abstract_inverted_index.through | 74, 119 |
| abstract_inverted_index.trained | 95 |
| abstract_inverted_index.whether | 148 |
| abstract_inverted_index.without | 53 |
| abstract_inverted_index.Finally, | 129 |
| abstract_inverted_index.accuracy | 174 |
| abstract_inverted_index.achieved | 73 |
| abstract_inverted_index.assembly | 1, 29, 40, 51, 188 |
| abstract_inverted_index.datasets | 21 |
| abstract_inverted_index.guidance | 184 |
| abstract_inverted_index.labeling | 70 |
| abstract_inverted_index.multiple | 134 |
| abstract_inverted_index.physical | 28, 54, 187 |
| abstract_inverted_index.process. | 193 |
| abstract_inverted_index.utilized | 60 |
| abstract_inverted_index.Fast-RCNN | 121 |
| abstract_inverted_index.assembly, | 170 |
| abstract_inverted_index.assembly. | 55, 155 |
| abstract_inverted_index.automatic | 69 |
| abstract_inverted_index.candidate | 116 |
| abstract_inverted_index.challenge | 34 |
| abstract_inverted_index.comparing | 140 |
| abstract_inverted_index.datasets. | 98 |
| abstract_inverted_index.detecting | 131 |
| abstract_inverted_index.detection | 88, 103, 117 |
| abstract_inverted_index.dimension | 137 |
| abstract_inverted_index.enclosing | 79 |
| abstract_inverted_index.features, | 108 |
| abstract_inverted_index.generated | 111 |
| abstract_inverted_index.inability | 24 |
| abstract_inverted_index.integrity | 52, 167 |
| abstract_inverted_index.rectangle | 80 |
| abstract_inverted_index.reduction | 138 |
| abstract_inverted_index.rendering | 64 |
| abstract_inverted_index.Incomplete | 0 |
| abstract_inverted_index.accurately | 164 |
| abstract_inverted_index.determines | 114 |
| abstract_inverted_index.end-to-end | 87 |
| abstract_inverted_index.extraction | 76 |
| abstract_inverted_index.industrial | 15 |
| abstract_inverted_index.integrity. | 41 |
| abstract_inverted_index.knowledge, | 144 |
| abstract_inverted_index.Faster-RCNN | 93 |
| abstract_inverted_index.inestimable | 11 |
| abstract_inverted_index.model-based | 47 |
| abstract_inverted_index.theoretical | 183 |
| abstract_inverted_index.Experimental | 156 |
| abstract_inverted_index.environment, | 16 |
| abstract_inverted_index.experimental | 192 |
| abstract_inverted_index.performance, | 7 |
| abstract_inverted_index.88.9%. | 177 |
| abstract_inverted_index.construction, | 67 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5299999713897705 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.644934 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |