Using Attention for Improving Defect Detection in Existing RC Bridges
In the constantly growing need for sustainable mobility and transportation, on-site inspections of existing reinforced concrete (RC) bridges are critical in ensuring the safety of such infrastructures. However, surveying RC bridges presents several challenges, such as the high costs and effort requi...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10849534/ |
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| author | Sergio Ruggieri Angelo Cardellicchio Andrea Nettis Vito Reno Giuseppina Uva |
| author_facet | Sergio Ruggieri Angelo Cardellicchio Andrea Nettis Vito Reno Giuseppina Uva |
| author_sort | Sergio Ruggieri |
| collection | DOAJ |
| description | In the constantly growing need for sustainable mobility and transportation, on-site inspections of existing reinforced concrete (RC) bridges are critical in ensuring the safety of such infrastructures. However, surveying RC bridges presents several challenges, such as the high costs and effort required by the surveyors, the subjectivity in assessing identified defects, and the possible lapses of attention when inspections are systematically repeated on different bridges. Hence, traditional methods of on-site inspection can be enhanced by leveraging digital innovations and by developing new instruments that support road management companies in ensuring the safety of the existing infrastructure. Among the new technologies, deep learning-based object detection systems provide promising and effective solutions. As such, this research proposes a new, simple, intuitive and efficient tool to support engineers and surveyors in assessing the health state of existing RC bridges. To this end, domain experts gathered and labelled a dataset of real images containing typical defects found in existing RC bridges. Consequently, an improved version of YOLO11, embedding attention mechanisms to allow the network to focus on the most relevant details in each image, was trained, tested, and validated on the provided dataset, showing an overall improvement of quantitative metrics such as precision and recall, while retaining enough computational efficiency to allow real-time implementation on constrained devices. Visual explanations achieved via the Eigen-CAM algorithm were also exploited to evaluate the reliability of the predictions. The model was finally embedded in an end-to-end tool offering a graphical user interface (GUI) to allow an effective interaction between the domain expert and the machine. Overall, the proposal revealed its potential to improve the effectiveness of the survey, lowering the burden on surveyors and engineers and providing a reliable method to improve the overall security in large RC bridges portfolios. |
| format | Article |
| id | doaj-art-6d8f8001a76b409abd15b92bd3b2b397 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-6d8f8001a76b409abd15b92bd3b2b3972025-01-31T00:01:36ZengIEEEIEEE Access2169-35362025-01-0113189941901510.1109/ACCESS.2025.353283210849534Using Attention for Improving Defect Detection in Existing RC BridgesSergio Ruggieri0https://orcid.org/0000-0001-5119-8967Angelo Cardellicchio1https://orcid.org/0000-0003-3313-4817Andrea Nettis2https://orcid.org/0000-0001-8133-6830Vito Reno3https://orcid.org/0000-0003-1830-4961Giuseppina Uva4DICATECH Department, Polytechnic University of Bari, Bari, ItalyInstitute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Bari, ItalyDICATECH Department, Polytechnic University of Bari, Bari, ItalyInstitute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Bari, ItalyDICATECH Department, Polytechnic University of Bari, Bari, ItalyIn the constantly growing need for sustainable mobility and transportation, on-site inspections of existing reinforced concrete (RC) bridges are critical in ensuring the safety of such infrastructures. However, surveying RC bridges presents several challenges, such as the high costs and effort required by the surveyors, the subjectivity in assessing identified defects, and the possible lapses of attention when inspections are systematically repeated on different bridges. Hence, traditional methods of on-site inspection can be enhanced by leveraging digital innovations and by developing new instruments that support road management companies in ensuring the safety of the existing infrastructure. Among the new technologies, deep learning-based object detection systems provide promising and effective solutions. As such, this research proposes a new, simple, intuitive and efficient tool to support engineers and surveyors in assessing the health state of existing RC bridges. To this end, domain experts gathered and labelled a dataset of real images containing typical defects found in existing RC bridges. Consequently, an improved version of YOLO11, embedding attention mechanisms to allow the network to focus on the most relevant details in each image, was trained, tested, and validated on the provided dataset, showing an overall improvement of quantitative metrics such as precision and recall, while retaining enough computational efficiency to allow real-time implementation on constrained devices. Visual explanations achieved via the Eigen-CAM algorithm were also exploited to evaluate the reliability of the predictions. The model was finally embedded in an end-to-end tool offering a graphical user interface (GUI) to allow an effective interaction between the domain expert and the machine. Overall, the proposal revealed its potential to improve the effectiveness of the survey, lowering the burden on surveyors and engineers and providing a reliable method to improve the overall security in large RC bridges portfolios.https://ieeexplore.ieee.org/document/10849534/Existing bridgessurface defectsdeep-learningobject detectionpractice-oriented tool |
| spellingShingle | Sergio Ruggieri Angelo Cardellicchio Andrea Nettis Vito Reno Giuseppina Uva Using Attention for Improving Defect Detection in Existing RC Bridges IEEE Access Existing bridges surface defects deep-learning object detection practice-oriented tool |
| title | Using Attention for Improving Defect Detection in Existing RC Bridges |
| title_full | Using Attention for Improving Defect Detection in Existing RC Bridges |
| title_fullStr | Using Attention for Improving Defect Detection in Existing RC Bridges |
| title_full_unstemmed | Using Attention for Improving Defect Detection in Existing RC Bridges |
| title_short | Using Attention for Improving Defect Detection in Existing RC Bridges |
| title_sort | using attention for improving defect detection in existing rc bridges |
| topic | Existing bridges surface defects deep-learning object detection practice-oriented tool |
| url | https://ieeexplore.ieee.org/document/10849534/ |
| work_keys_str_mv | AT sergioruggieri usingattentionforimprovingdefectdetectioninexistingrcbridges AT angelocardellicchio usingattentionforimprovingdefectdetectioninexistingrcbridges AT andreanettis usingattentionforimprovingdefectdetectioninexistingrcbridges AT vitoreno usingattentionforimprovingdefectdetectioninexistingrcbridges AT giuseppinauva usingattentionforimprovingdefectdetectioninexistingrcbridges |