Feasibility of EfficientDet-D3 for Accurate and Efficient Void Detection in GPR Images Article Swipe
Sung-Pil Shin
,
Sang-Yum Lee
,
Tri Ho Minh Le
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/infrastructures10060140
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/infrastructures10060140
The detection of voids in pavement infrastructure is essential for road safety and efficient maintenance. Traditional methods of analyzing ground-penetrating radar (GPR) data are labor-intensive and error-prone. This study presents a novel approach using the EfficientDet-D3 deep learning model for automated void detection in GPR images. The model combines advanced feature extraction and compound scaling to balance accuracy and computational efficiency, making it suitable for real-time applications. A diverse GPR image dataset, including various pavement types and environmental conditions, was curated and preprocessed to improve model generalization. The model was fine-tuned through hyperparameter optimization, achieving a precision of 91.2%, a recall of 87.5%, and an F1-score of 89.3%. It also attained mean Average Precision (mAP) values of 89.7% at IoU 0.5 and 84.3% at IoU 0.75, demonstrating strong localization performance. Comparative analysis with models such as YOLOv8 and Mask R-CNN shows that EfficientDet-D3 offers a superior balance between accuracy and inference speed, with an inference time of 68 ms. This research provides a scalable, efficient solution for pavement void detection, paving the way for integrating deep learning models into pavement management systems to enhance infrastructure sustainability. Future work will focus on model optimization and expanding dataset diversity.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/infrastructures10060140
- https://www.mdpi.com/2412-3811/10/6/140/pdf?version=1749132228
- OA Status
- gold
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4411057800Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/infrastructures10060140Digital Object Identifier
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Feasibility of EfficientDet-D3 for Accurate and Efficient Void Detection in GPR ImagesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-06-05Full publication date if available
- Authors
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Sung-Pil Shin, Sang-Yum Lee, Tri Ho Minh LeList of authors in order
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https://doi.org/10.3390/infrastructures10060140Publisher landing page
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https://www.mdpi.com/2412-3811/10/6/140/pdf?version=1749132228Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2412-3811/10/6/140/pdf?version=1749132228Direct OA link when available
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Ground-penetrating radar, Void (composites), Computer science, Remote sensing, Geology, Artificial intelligence, Materials science, Radar, Composite material, TelecommunicationsTop 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.(mAP) | 114 |
| abstract_inverted_index.0.75, | 125 |
| abstract_inverted_index.84.3% | 122 |
| abstract_inverted_index.89.7% | 117 |
| abstract_inverted_index.R-CNN | 139 |
| abstract_inverted_index.focus | 189 |
| abstract_inverted_index.image | 70 |
| abstract_inverted_index.model | 38, 47, 85, 88, 191 |
| abstract_inverted_index.novel | 31 |
| abstract_inverted_index.radar | 20 |
| abstract_inverted_index.shows | 140 |
| abstract_inverted_index.study | 28 |
| abstract_inverted_index.types | 75 |
| abstract_inverted_index.using | 33 |
| abstract_inverted_index.voids | 3 |
| abstract_inverted_index.87.5%, | 102 |
| abstract_inverted_index.89.3%. | 107 |
| abstract_inverted_index.91.2%, | 98 |
| abstract_inverted_index.Future | 186 |
| abstract_inverted_index.YOLOv8 | 136 |
| abstract_inverted_index.making | 61 |
| abstract_inverted_index.models | 133, 177 |
| abstract_inverted_index.offers | 143 |
| abstract_inverted_index.paving | 170 |
| abstract_inverted_index.recall | 100 |
| abstract_inverted_index.safety | 11 |
| abstract_inverted_index.speed, | 151 |
| abstract_inverted_index.strong | 127 |
| abstract_inverted_index.values | 115 |
| abstract_inverted_index.Average | 112 |
| abstract_inverted_index.balance | 56, 146 |
| abstract_inverted_index.between | 147 |
| abstract_inverted_index.curated | 80 |
| abstract_inverted_index.dataset | 195 |
| abstract_inverted_index.diverse | 68 |
| abstract_inverted_index.enhance | 183 |
| abstract_inverted_index.feature | 50 |
| abstract_inverted_index.images. | 45 |
| abstract_inverted_index.improve | 84 |
| abstract_inverted_index.methods | 16 |
| abstract_inverted_index.scaling | 54 |
| abstract_inverted_index.systems | 181 |
| abstract_inverted_index.through | 91 |
| abstract_inverted_index.various | 73 |
| abstract_inverted_index.F1-score | 105 |
| abstract_inverted_index.accuracy | 57, 148 |
| abstract_inverted_index.advanced | 49 |
| abstract_inverted_index.analysis | 131 |
| abstract_inverted_index.approach | 32 |
| abstract_inverted_index.attained | 110 |
| abstract_inverted_index.combines | 48 |
| abstract_inverted_index.compound | 53 |
| abstract_inverted_index.dataset, | 71 |
| abstract_inverted_index.learning | 37, 176 |
| abstract_inverted_index.pavement | 5, 74, 167, 179 |
| abstract_inverted_index.presents | 29 |
| abstract_inverted_index.provides | 161 |
| abstract_inverted_index.research | 160 |
| abstract_inverted_index.solution | 165 |
| abstract_inverted_index.suitable | 63 |
| abstract_inverted_index.superior | 145 |
| abstract_inverted_index.Precision | 113 |
| abstract_inverted_index.achieving | 94 |
| abstract_inverted_index.analyzing | 18 |
| abstract_inverted_index.automated | 40 |
| abstract_inverted_index.detection | 1, 42 |
| abstract_inverted_index.efficient | 13, 164 |
| abstract_inverted_index.essential | 8 |
| abstract_inverted_index.expanding | 194 |
| abstract_inverted_index.including | 72 |
| abstract_inverted_index.inference | 150, 154 |
| abstract_inverted_index.precision | 96 |
| abstract_inverted_index.real-time | 65 |
| abstract_inverted_index.scalable, | 163 |
| abstract_inverted_index.detection, | 169 |
| abstract_inverted_index.diversity. | 196 |
| abstract_inverted_index.extraction | 51 |
| abstract_inverted_index.fine-tuned | 90 |
| abstract_inverted_index.management | 180 |
| abstract_inverted_index.Comparative | 130 |
| abstract_inverted_index.Traditional | 15 |
| abstract_inverted_index.conditions, | 78 |
| abstract_inverted_index.efficiency, | 60 |
| abstract_inverted_index.integrating | 174 |
| abstract_inverted_index.error-prone. | 26 |
| abstract_inverted_index.localization | 128 |
| abstract_inverted_index.maintenance. | 14 |
| abstract_inverted_index.optimization | 192 |
| abstract_inverted_index.performance. | 129 |
| abstract_inverted_index.preprocessed | 82 |
| abstract_inverted_index.applications. | 66 |
| abstract_inverted_index.computational | 59 |
| abstract_inverted_index.demonstrating | 126 |
| abstract_inverted_index.environmental | 77 |
| abstract_inverted_index.optimization, | 93 |
| abstract_inverted_index.hyperparameter | 92 |
| abstract_inverted_index.infrastructure | 6, 184 |
| abstract_inverted_index.EfficientDet-D3 | 35, 142 |
| abstract_inverted_index.generalization. | 86 |
| abstract_inverted_index.labor-intensive | 24 |
| abstract_inverted_index.sustainability. | 185 |
| abstract_inverted_index.ground-penetrating | 19 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.84812838 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |