PPDD: Egocentric Crack Segmentation in the Port Pavement with Deep Learning-Based Methods
Road infrastructure is a critical component of modern society, with its maintenance directly influencing traffic safety and logistical efficiency. In this context, automated crack detection technology plays a vital role in reducing maintenance costs and enhancing operational efficiency. However, pre...
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MDPI AG
2025-05-01
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| author | Hyemin Yoon Hoe-Kyoung Kim Sangjin Kim |
| author_facet | Hyemin Yoon Hoe-Kyoung Kim Sangjin Kim |
| author_sort | Hyemin Yoon |
| collection | DOAJ |
| description | Road infrastructure is a critical component of modern society, with its maintenance directly influencing traffic safety and logistical efficiency. In this context, automated crack detection technology plays a vital role in reducing maintenance costs and enhancing operational efficiency. However, previous studies are limited by the fact that they provide only bounding box or segmentation mask annotations for a restricted number of crack classes and use a relatively small size of datasets. To address these limitations and advance deep learning-based crack segmentation, this study introduces a novel crack segmentation dataset that reflects real-world road conditions. The proposed dataset includes various types of cracks and defects—such as slippage, rutting, and construction-related cracks—and provides polygon-based segmentation masks captured from an egocentric, vehicle-mounted perspective. Using this dataset, we evaluated the performance of semantic and instance segmentation models. Notably, SegFormer achieved the highest Pixel Accuracy (PA) and mean Intersection over Union (mIoU) for semantic segmentation, while YOLOv7 exhibited outstanding detection performance for alligator crack class, recording an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>A</mi><mi>P</mi></mrow><mrow><mn>50</mn></mrow></msub></mrow></semantics></math></inline-formula> of 87.2% and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>A</mi><mi>P</mi></mrow></semantics></math></inline-formula> of 57.5%. In contrast, all models struggled with the reflection crack type, indicating the inherent segmentation challenges. Overall, this study provides a practical and robust foundation for future research in automated road crack segmentation. Additional resources including the dataset and annotation details can be found at our GitHub repository. |
| format | Article |
| id | doaj-art-31fe3e28a85b4cc19ac18cca8dc769bb |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
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| series | Applied Sciences |
| spelling | doaj-art-31fe3e28a85b4cc19ac18cca8dc769bb2025-08-20T01:56:14ZengMDPI AGApplied Sciences2076-34172025-05-011510544610.3390/app15105446PPDD: Egocentric Crack Segmentation in the Port Pavement with Deep Learning-Based MethodsHyemin Yoon0Hoe-Kyoung Kim1Sangjin Kim2Department of Management Information Systems, Dong-A University, Busan 49236, Republic of KoreaDepartment of Urban Planning and Engineering, Dong-A University, Busan 49315, Republic of KoreaDepartment of Management Information Systems, Dong-A University, Busan 49236, Republic of KoreaRoad infrastructure is a critical component of modern society, with its maintenance directly influencing traffic safety and logistical efficiency. In this context, automated crack detection technology plays a vital role in reducing maintenance costs and enhancing operational efficiency. However, previous studies are limited by the fact that they provide only bounding box or segmentation mask annotations for a restricted number of crack classes and use a relatively small size of datasets. To address these limitations and advance deep learning-based crack segmentation, this study introduces a novel crack segmentation dataset that reflects real-world road conditions. The proposed dataset includes various types of cracks and defects—such as slippage, rutting, and construction-related cracks—and provides polygon-based segmentation masks captured from an egocentric, vehicle-mounted perspective. Using this dataset, we evaluated the performance of semantic and instance segmentation models. Notably, SegFormer achieved the highest Pixel Accuracy (PA) and mean Intersection over Union (mIoU) for semantic segmentation, while YOLOv7 exhibited outstanding detection performance for alligator crack class, recording an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>A</mi><mi>P</mi></mrow><mrow><mn>50</mn></mrow></msub></mrow></semantics></math></inline-formula> of 87.2% and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>A</mi><mi>P</mi></mrow></semantics></math></inline-formula> of 57.5%. In contrast, all models struggled with the reflection crack type, indicating the inherent segmentation challenges. Overall, this study provides a practical and robust foundation for future research in automated road crack segmentation. Additional resources including the dataset and annotation details can be found at our GitHub repository.https://www.mdpi.com/2076-3417/15/10/5446semantic segmentationinstance segmentationdeep learningport road crackvehicle-egocentric view datadata analysis |
| spellingShingle | Hyemin Yoon Hoe-Kyoung Kim Sangjin Kim PPDD: Egocentric Crack Segmentation in the Port Pavement with Deep Learning-Based Methods Applied Sciences semantic segmentation instance segmentation deep learning port road crack vehicle-egocentric view data data analysis |
| title | PPDD: Egocentric Crack Segmentation in the Port Pavement with Deep Learning-Based Methods |
| title_full | PPDD: Egocentric Crack Segmentation in the Port Pavement with Deep Learning-Based Methods |
| title_fullStr | PPDD: Egocentric Crack Segmentation in the Port Pavement with Deep Learning-Based Methods |
| title_full_unstemmed | PPDD: Egocentric Crack Segmentation in the Port Pavement with Deep Learning-Based Methods |
| title_short | PPDD: Egocentric Crack Segmentation in the Port Pavement with Deep Learning-Based Methods |
| title_sort | ppdd egocentric crack segmentation in the port pavement with deep learning based methods |
| topic | semantic segmentation instance segmentation deep learning port road crack vehicle-egocentric view data data analysis |
| url | https://www.mdpi.com/2076-3417/15/10/5446 |
| work_keys_str_mv | AT hyeminyoon ppddegocentriccracksegmentationintheportpavementwithdeeplearningbasedmethods AT hoekyoungkim ppddegocentriccracksegmentationintheportpavementwithdeeplearningbasedmethods AT sangjinkim ppddegocentriccracksegmentationintheportpavementwithdeeplearningbasedmethods |