Automatic Detection of Pothole Distress in Asphalt Pavement Using Improved Convolutional Neural Networks
To realize the intelligent and accurate measurement of pavement surface potholes, an improved You Only Look Once version three (YOLOv3) object detection model combining data augmentation and structure optimization is proposed in this study. First, color adjustment was used to enhance the image contr...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/16/3892 |
| _version_ | 1797431787098472448 |
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| author | Danyu Wang Zhen Liu Xingyu Gu Wenxiu Wu Yihan Chen Lutai Wang |
| author_facet | Danyu Wang Zhen Liu Xingyu Gu Wenxiu Wu Yihan Chen Lutai Wang |
| author_sort | Danyu Wang |
| collection | DOAJ |
| description | To realize the intelligent and accurate measurement of pavement surface potholes, an improved You Only Look Once version three (YOLOv3) object detection model combining data augmentation and structure optimization is proposed in this study. First, color adjustment was used to enhance the image contrast, and data augmentation was performed through geometric transformation. Pothole categories were subdivided into P1 and P2 on the basis of whether or not there was water. Then, the Residual Network (ResNet101) and complete IoU (CIoU) loss were used to optimize the structure of the YOLOv3 model, and the K-Means++ algorithm was used to cluster and modify the multiscale anchor sizes. Lastly, the robustness of the proposed model was assessed by generating adversarial examples. Experimental results demonstrated that the proposed model was significantly improved compared with the original YOLOv3 model; the detection mean average precision (mAP) was 89.3%, and the F1-score was 86.5%. On the attacked testing dataset, the overall mAP value reached 81.2% (−8.1%), which shows that this proposed model performed well on samples after random occlusion and adding noise interference, proving good robustness. |
| first_indexed | 2024-03-09T09:50:21Z |
| format | Article |
| id | doaj.art-ccf8027c8d224ab88ee625e45d186114 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T09:50:21Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-ccf8027c8d224ab88ee625e45d1861142023-12-02T00:14:48ZengMDPI AGRemote Sensing2072-42922022-08-011416389210.3390/rs14163892Automatic Detection of Pothole Distress in Asphalt Pavement Using Improved Convolutional Neural NetworksDanyu Wang0Zhen Liu1Xingyu Gu2Wenxiu Wu3Yihan Chen4Lutai Wang5Department of Roadway Engineering, School of Transportation, Southeast University, Nanjing 211189, ChinaDepartment of Roadway Engineering, School of Transportation, Southeast University, Nanjing 211189, ChinaDepartment of Roadway Engineering, School of Transportation, Southeast University, Nanjing 211189, ChinaJinhua Highway Administration Bureau, Jinhua 321000, ChinaDepartment of Roadway Engineering, School of Transportation, Southeast University, Nanjing 211189, ChinaDepartment of Roadway Engineering, School of Transportation, Southeast University, Nanjing 211189, ChinaTo realize the intelligent and accurate measurement of pavement surface potholes, an improved You Only Look Once version three (YOLOv3) object detection model combining data augmentation and structure optimization is proposed in this study. First, color adjustment was used to enhance the image contrast, and data augmentation was performed through geometric transformation. Pothole categories were subdivided into P1 and P2 on the basis of whether or not there was water. Then, the Residual Network (ResNet101) and complete IoU (CIoU) loss were used to optimize the structure of the YOLOv3 model, and the K-Means++ algorithm was used to cluster and modify the multiscale anchor sizes. Lastly, the robustness of the proposed model was assessed by generating adversarial examples. Experimental results demonstrated that the proposed model was significantly improved compared with the original YOLOv3 model; the detection mean average precision (mAP) was 89.3%, and the F1-score was 86.5%. On the attacked testing dataset, the overall mAP value reached 81.2% (−8.1%), which shows that this proposed model performed well on samples after random occlusion and adding noise interference, proving good robustness.https://www.mdpi.com/2072-4292/14/16/3892pavement distresspothole detectionYOLOv3data augmentationrobustness |
| spellingShingle | Danyu Wang Zhen Liu Xingyu Gu Wenxiu Wu Yihan Chen Lutai Wang Automatic Detection of Pothole Distress in Asphalt Pavement Using Improved Convolutional Neural Networks Remote Sensing pavement distress pothole detection YOLOv3 data augmentation robustness |
| title | Automatic Detection of Pothole Distress in Asphalt Pavement Using Improved Convolutional Neural Networks |
| title_full | Automatic Detection of Pothole Distress in Asphalt Pavement Using Improved Convolutional Neural Networks |
| title_fullStr | Automatic Detection of Pothole Distress in Asphalt Pavement Using Improved Convolutional Neural Networks |
| title_full_unstemmed | Automatic Detection of Pothole Distress in Asphalt Pavement Using Improved Convolutional Neural Networks |
| title_short | Automatic Detection of Pothole Distress in Asphalt Pavement Using Improved Convolutional Neural Networks |
| title_sort | automatic detection of pothole distress in asphalt pavement using improved convolutional neural networks |
| topic | pavement distress pothole detection YOLOv3 data augmentation robustness |
| url | https://www.mdpi.com/2072-4292/14/16/3892 |
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