Deep Learning Approach for Volume Estimation in Earthmoving Operation Article Swipe
Faria Alam
,
Hoo Sang Ko
,
H. Felix Lee
,
Chenxi Yuan
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.24867/ijiem-2023-1-323
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.24867/ijiem-2023-1-323
Earthmoving is a significant activity in most heavy structural designing projects. Earthmoving volume is typically assessed by counting the number of stacked trucks and weighing them on a scale; however, these strategies are error-prone and costly. To address the challenge, this study investigated a deep learning approach for estimating earth volume from photo images of loaded trucks. First, a basic classification model with one convolutional layer has been developed to estimate earth volume by classifying the images into different levels. Next, we applied transfer learning to a pre-trained deep convolutional neural network in order to improve classification performance. For evaluation of the approach, the models have been trained and tested by using images of miniature trucks loaded with different amounts of earth, ranging between 0 and 1000 ml up to six classes at 200 ml intervals. The experimental results showed that the pre-trained network with transfer learning achieved more than 90% accuracy in most cases. The results indicate that the proposed approach has the potential in estimating earth volume in trucks in real-time with minimal intervention by taking images.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.24867/ijiem-2023-1-323
- https://doi.org/10.24867/ijiem-2023-1-323
- OA Status
- diamond
- Cited By
- 7
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4324139178
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4324139178Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.24867/ijiem-2023-1-323Digital Object Identifier
- Title
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Deep Learning Approach for Volume Estimation in Earthmoving OperationWork 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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2023-03-14Full publication date if available
- Authors
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Faria Alam, Hoo Sang Ko, H. Felix Lee, Chenxi YuanList of authors in order
- Landing page
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https://doi.org/10.24867/ijiem-2023-1-323Publisher landing page
- PDF URL
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https://doi.org/10.24867/ijiem-2023-1-323Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
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https://doi.org/10.24867/ijiem-2023-1-323Direct OA link when available
- Concepts
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Transfer of learning, Truck, Volume (thermodynamics), Convolutional neural network, Deep learning, Artificial intelligence, Computer science, Ranging, Artificial neural network, Machine learning, Pattern recognition (psychology), Engineering, Automotive engineering, Quantum mechanics, Physics, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
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7Total citation count in OpenAlex
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2025: 2, 2024: 3, 2023: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.cases. | 154 |
| abstract_inverted_index.earth, | 121 |
| abstract_inverted_index.images | 53, 76, 112 |
| abstract_inverted_index.loaded | 55, 116 |
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| abstract_inverted_index.neural | 90 |
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| abstract_inverted_index.scale; | 28 |
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| abstract_inverted_index.taking | 177 |
| abstract_inverted_index.tested | 109 |
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| abstract_inverted_index.applied | 82 |
| abstract_inverted_index.between | 123 |
| abstract_inverted_index.classes | 131 |
| abstract_inverted_index.costly. | 35 |
| abstract_inverted_index.images. | 178 |
| abstract_inverted_index.improve | 95 |
| abstract_inverted_index.levels. | 79 |
| abstract_inverted_index.minimal | 174 |
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| abstract_inverted_index.ranging | 122 |
| abstract_inverted_index.results | 138, 156 |
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| abstract_inverted_index.miniature | 114 |
| abstract_inverted_index.potential | 164 |
| abstract_inverted_index.projects. | 10 |
| abstract_inverted_index.real-time | 172 |
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| abstract_inverted_index.strategies | 31 |
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| abstract_inverted_index.convolutional | 64, 89 |
| abstract_inverted_index.classification | 60, 96 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.6000000238418579 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.77671351 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |