Automated Asphalt Highway Pavement Crack Detection Based on Deformable Single Shot Multi-Box Detector Under a Complex Environment
Pavement cracks are severely affecting highway performance. Thus, implementing high-precision highway pavement crack detection is important for highway maintenance. However, the asphalt highway pavement environment is complex, and different pavement backgrounds are more difficult than others for det...
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/cd4f544206254b87b12f025f235f2e8a |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | Pavement cracks are severely affecting highway performance. Thus, implementing high-precision highway pavement crack detection is important for highway maintenance. However, the asphalt highway pavement environment is complex, and different pavement backgrounds are more difficult than others for detecting highway pavement cracks. Interference from road markings and surface repairs also complicate the environments and thus the detection of crack. To reduce interference, we collected many images from different highway pavement backgrounds. We also improved the single shot multi-box detector (SSD) network and proposed a novel network named deformable SSD by adding a deformable convolution to the backbone feature extraction network VGG16. We verified our model using the PASCAL VOC2007 dataset and obtained a mean average precision (<italic>mAP</italic>) 3.1% higher than that of the original SSD model. We then trained and tested the proposed model using our crack detection dataset. We calculated precision, recall, F1 score, AP, <italic>mAP</italic>, and FPS to examine the performance of our model. The <italic>mAP</italic> of all categories in the test data was 85.11% using the proposed model 10.4% and 0.55% more than that of YOLOv4 and the original SSD model, respectively. These findings show that our model outperforms YOLOv4 and the original SSD model and confirm that incorporating a deformable convolution into the SSD network can improve the model’s performance. The proposed model is appropriate for detecting pavement crack categories and locations in complicated environments. It can also provide important technical support for highway maintenance. |
|---|