Analysis and Classification of Distress on Flexible Pavements Using Convolutional Neural Networks: A Case Study in Benin Republic
Roads are critical infrastructure in multi-sectoral development. Any country that aims to expand and stabilize its activities must have a network of paved roads in good condition. However, that is not the case in many countries. The usual methods of recording and classifying pavement distress on the...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Infrastructures |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2412-3811/10/5/111 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849326985405267968 |
|---|---|
| author | Crespin Prudence Yabi Godfree F. Gbehoun Bio Chéissou Koto Tamou Eric Alamou Mohamed Gibigaye Ehsan Noroozinejad Farsangi |
| author_facet | Crespin Prudence Yabi Godfree F. Gbehoun Bio Chéissou Koto Tamou Eric Alamou Mohamed Gibigaye Ehsan Noroozinejad Farsangi |
| author_sort | Crespin Prudence Yabi |
| collection | DOAJ |
| description | Roads are critical infrastructure in multi-sectoral development. Any country that aims to expand and stabilize its activities must have a network of paved roads in good condition. However, that is not the case in many countries. The usual methods of recording and classifying pavement distress on the roads require a lot of equipment, technicians, and time to obtain the nature and indices of the damage to estimate the roadway’s quality level. This study proposes the use of pavement distress detection and classification models based on Convolutional Neural Networks, starting from videos taken of any asphalt road. To carry out this work, various routes were filmed to list the degradations concerned. Images were extracted from these videos and then resized and annotated. Then, these images were used to constitute several databases of road damage, such as longitudinal cracks, alligator cracks, small potholes, and patching. Within an appropriate development environment, three Convolutional Neural Networks were developed and trained on the databases. The accuracy achieved by the different models varies from 94.6% to 97.3%. This accuracy is promising compared to the literature models. This method would make it possible to considerably reduce the financial resources used for each road data campaign. |
| format | Article |
| id | doaj-art-1e16d6c8b60b482c8f98670ad6a3b1e8 |
| institution | Kabale University |
| issn | 2412-3811 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Infrastructures |
| spelling | doaj-art-1e16d6c8b60b482c8f98670ad6a3b1e82025-08-20T03:47:59ZengMDPI AGInfrastructures2412-38112025-04-0110511110.3390/infrastructures10050111Analysis and Classification of Distress on Flexible Pavements Using Convolutional Neural Networks: A Case Study in Benin RepublicCrespin Prudence Yabi0Godfree F. Gbehoun1Bio Chéissou Koto Tamou2Eric Alamou3Mohamed Gibigaye4Ehsan Noroozinejad Farsangi5Laboratory of Studies and Tests in Civil Engineering (L2EGC), National University of Sciences, Technologies, Engineering and Mathematics, Abomey BP 486, BeninLaboratory of Studies and Tests in Civil Engineering (L2EGC), National University of Sciences, Technologies, Engineering and Mathematics, Abomey BP 486, BeninLaboratory of Studies and Tests in Civil Engineering (L2EGC), National University of Sciences, Technologies, Engineering and Mathematics, Abomey BP 486, BeninLaboratory of Studies and Tests in Civil Engineering (L2EGC), National University of Sciences, Technologies, Engineering and Mathematics, Abomey BP 486, BeninLaboratory of Applied Energetic and Mechanic (LEMA), University of Abomey-Calavi, Abomey-Calavi 01BP 526, BeninUrban Transformations Research Centre (UTRC), Western Sydney University, Parramatta, NSW 2150, AustraliaRoads are critical infrastructure in multi-sectoral development. Any country that aims to expand and stabilize its activities must have a network of paved roads in good condition. However, that is not the case in many countries. The usual methods of recording and classifying pavement distress on the roads require a lot of equipment, technicians, and time to obtain the nature and indices of the damage to estimate the roadway’s quality level. This study proposes the use of pavement distress detection and classification models based on Convolutional Neural Networks, starting from videos taken of any asphalt road. To carry out this work, various routes were filmed to list the degradations concerned. Images were extracted from these videos and then resized and annotated. Then, these images were used to constitute several databases of road damage, such as longitudinal cracks, alligator cracks, small potholes, and patching. Within an appropriate development environment, three Convolutional Neural Networks were developed and trained on the databases. The accuracy achieved by the different models varies from 94.6% to 97.3%. This accuracy is promising compared to the literature models. This method would make it possible to considerably reduce the financial resources used for each road data campaign.https://www.mdpi.com/2412-3811/10/5/111asphalt pavementpavement distress classificationimage classificationroad maintenanceconvolutional neural network |
| spellingShingle | Crespin Prudence Yabi Godfree F. Gbehoun Bio Chéissou Koto Tamou Eric Alamou Mohamed Gibigaye Ehsan Noroozinejad Farsangi Analysis and Classification of Distress on Flexible Pavements Using Convolutional Neural Networks: A Case Study in Benin Republic Infrastructures asphalt pavement pavement distress classification image classification road maintenance convolutional neural network |
| title | Analysis and Classification of Distress on Flexible Pavements Using Convolutional Neural Networks: A Case Study in Benin Republic |
| title_full | Analysis and Classification of Distress on Flexible Pavements Using Convolutional Neural Networks: A Case Study in Benin Republic |
| title_fullStr | Analysis and Classification of Distress on Flexible Pavements Using Convolutional Neural Networks: A Case Study in Benin Republic |
| title_full_unstemmed | Analysis and Classification of Distress on Flexible Pavements Using Convolutional Neural Networks: A Case Study in Benin Republic |
| title_short | Analysis and Classification of Distress on Flexible Pavements Using Convolutional Neural Networks: A Case Study in Benin Republic |
| title_sort | analysis and classification of distress on flexible pavements using convolutional neural networks a case study in benin republic |
| topic | asphalt pavement pavement distress classification image classification road maintenance convolutional neural network |
| url | https://www.mdpi.com/2412-3811/10/5/111 |
| work_keys_str_mv | AT crespinprudenceyabi analysisandclassificationofdistressonflexiblepavementsusingconvolutionalneuralnetworksacasestudyinbeninrepublic AT godfreefgbehoun analysisandclassificationofdistressonflexiblepavementsusingconvolutionalneuralnetworksacasestudyinbeninrepublic AT biocheissoukototamou analysisandclassificationofdistressonflexiblepavementsusingconvolutionalneuralnetworksacasestudyinbeninrepublic AT ericalamou analysisandclassificationofdistressonflexiblepavementsusingconvolutionalneuralnetworksacasestudyinbeninrepublic AT mohamedgibigaye analysisandclassificationofdistressonflexiblepavementsusingconvolutionalneuralnetworksacasestudyinbeninrepublic AT ehsannoroozinejadfarsangi analysisandclassificationofdistressonflexiblepavementsusingconvolutionalneuralnetworksacasestudyinbeninrepublic |