Object detection for robotic grasping using a cascade of convolutional networks Article Swipe
Vitek Rais
,
Petr Doležel
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/pc58330.2023.10217360
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/pc58330.2023.10217360
Robot guidance in industry is a significant issue that needs to be dealt with in modern manufacturing facilities. One of the common tasks in this area is the pick and place problem. For proper implementation of an automatic pick and place application using a robotic arm for object grasping, it is necessary to detect the accurate pose of the objects of interest. In this contribution, a novel engineering approach to object positioning, based on image processing is proposed. In this approach, the operation is composed of a cascade of convolutional neural networks. This cascade consists of 2 different types of networks. The first one is the object detection network called YOLOv5. It is used to process the raw image data from the scene to provide precise localization and determine the position of the objects of interest. After that, crops of the detected objects are created and processed by the second neural network, namely EfficientNet. This classification network is used to determine the rotation angle of the detected objects. The proposed approach provides a precision rate of 0.997 and a recall rate of 0.999 for locating and determining the correct position. For angle classification, EfficientNet provides an accuracy of 0.951. All tests are performed on the testing set of the legitimate positioning problem.
Related Topics
Concepts
Artificial intelligence
Computer science
Convolutional neural network
Computer vision
Object detection
Object (grammar)
Process (computing)
Cascade
Rotation (mathematics)
Position (finance)
Robot
Recall rate
Artificial neural network
Set (abstract data type)
Pattern recognition (psychology)
Engineering
Economics
Finance
Operating system
Chemical engineering
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/pc58330.2023.10217360
- OA Status
- green
- Cited By
- 4
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386075389
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386075389Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/pc58330.2023.10217360Digital Object Identifier
- Title
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Object detection for robotic grasping using a cascade of convolutional networksWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-06-06Full publication date if available
- Authors
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Vitek Rais, Petr DoleželList of authors in order
- Landing page
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https://doi.org/10.1109/pc58330.2023.10217360Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://hdl.handle.net/10195/83815Direct OA link when available
- Concepts
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Artificial intelligence, Computer science, Convolutional neural network, Computer vision, Object detection, Object (grammar), Process (computing), Cascade, Rotation (mathematics), Position (finance), Robot, Recall rate, Artificial neural network, Set (abstract data type), Pattern recognition (psychology), Engineering, Economics, Finance, Operating system, Chemical engineering, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2024: 4Per-year citation counts (last 5 years)
- References (count)
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23Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.from | 120 |
| abstract_inverted_index.pick | 28, 38 |
| abstract_inverted_index.pose | 56 |
| abstract_inverted_index.rate | 174, 180 |
| abstract_inverted_index.that | 8 |
| abstract_inverted_index.this | 24, 63, 79 |
| abstract_inverted_index.used | 113, 158 |
| abstract_inverted_index.with | 13 |
| abstract_inverted_index.0.997 | 176 |
| abstract_inverted_index.0.999 | 182 |
| abstract_inverted_index.After | 136 |
| abstract_inverted_index.Robot | 0 |
| abstract_inverted_index.angle | 163, 191 |
| abstract_inverted_index.based | 72 |
| abstract_inverted_index.crops | 138 |
| abstract_inverted_index.dealt | 12 |
| abstract_inverted_index.first | 102 |
| abstract_inverted_index.image | 74, 118 |
| abstract_inverted_index.issue | 7 |
| abstract_inverted_index.needs | 9 |
| abstract_inverted_index.novel | 66 |
| abstract_inverted_index.place | 30, 40 |
| abstract_inverted_index.scene | 122 |
| abstract_inverted_index.tasks | 22 |
| abstract_inverted_index.tests | 200 |
| abstract_inverted_index.that, | 137 |
| abstract_inverted_index.types | 98 |
| abstract_inverted_index.using | 42 |
| abstract_inverted_index.0.951. | 198 |
| abstract_inverted_index.called | 109 |
| abstract_inverted_index.common | 21 |
| abstract_inverted_index.detect | 53 |
| abstract_inverted_index.modern | 15 |
| abstract_inverted_index.namely | 152 |
| abstract_inverted_index.neural | 90, 150 |
| abstract_inverted_index.object | 47, 70, 106 |
| abstract_inverted_index.proper | 33 |
| abstract_inverted_index.recall | 179 |
| abstract_inverted_index.second | 149 |
| abstract_inverted_index.YOLOv5. | 110 |
| abstract_inverted_index.cascade | 87, 93 |
| abstract_inverted_index.correct | 188 |
| abstract_inverted_index.created | 144 |
| abstract_inverted_index.network | 108, 156 |
| abstract_inverted_index.objects | 59, 133, 142 |
| abstract_inverted_index.precise | 125 |
| abstract_inverted_index.process | 115 |
| abstract_inverted_index.provide | 124 |
| abstract_inverted_index.robotic | 44 |
| abstract_inverted_index.testing | 205 |
| abstract_inverted_index.accuracy | 196 |
| abstract_inverted_index.accurate | 55 |
| abstract_inverted_index.approach | 68, 170 |
| abstract_inverted_index.composed | 84 |
| abstract_inverted_index.consists | 94 |
| abstract_inverted_index.detected | 141, 166 |
| abstract_inverted_index.guidance | 1 |
| abstract_inverted_index.industry | 3 |
| abstract_inverted_index.locating | 184 |
| abstract_inverted_index.network, | 151 |
| abstract_inverted_index.objects. | 167 |
| abstract_inverted_index.position | 130 |
| abstract_inverted_index.problem. | 31, 211 |
| abstract_inverted_index.proposed | 169 |
| abstract_inverted_index.provides | 171, 194 |
| abstract_inverted_index.rotation | 162 |
| abstract_inverted_index.approach, | 80 |
| abstract_inverted_index.automatic | 37 |
| abstract_inverted_index.detection | 107 |
| abstract_inverted_index.determine | 128, 160 |
| abstract_inverted_index.different | 97 |
| abstract_inverted_index.grasping, | 48 |
| abstract_inverted_index.interest. | 61, 135 |
| abstract_inverted_index.necessary | 51 |
| abstract_inverted_index.networks. | 91, 100 |
| abstract_inverted_index.operation | 82 |
| abstract_inverted_index.performed | 202 |
| abstract_inverted_index.position. | 189 |
| abstract_inverted_index.precision | 173 |
| abstract_inverted_index.processed | 146 |
| abstract_inverted_index.proposed. | 77 |
| abstract_inverted_index.legitimate | 209 |
| abstract_inverted_index.processing | 75 |
| abstract_inverted_index.application | 41 |
| abstract_inverted_index.determining | 186 |
| abstract_inverted_index.engineering | 67 |
| abstract_inverted_index.facilities. | 17 |
| abstract_inverted_index.positioning | 210 |
| abstract_inverted_index.significant | 6 |
| abstract_inverted_index.EfficientNet | 193 |
| abstract_inverted_index.localization | 126 |
| abstract_inverted_index.positioning, | 71 |
| abstract_inverted_index.EfficientNet. | 153 |
| abstract_inverted_index.contribution, | 64 |
| abstract_inverted_index.convolutional | 89 |
| abstract_inverted_index.manufacturing | 16 |
| abstract_inverted_index.classification | 155 |
| abstract_inverted_index.implementation | 34 |
| abstract_inverted_index.classification, | 192 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.6499999761581421 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.68815115 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |