Novel Computer Tomography image enhancement deep neural networks for asphalt mixtures

Yang, Handuo; Huo, Ju; Ma, Tao; Tong, Zheng; Han, Chengjia; Xie, Tianyan

doi:10.1016/j.conbuildmat.2022.129067

Cited by 8 publications

(2 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Subsequently, anchors are devised to pinpoint pavement cracks in images [14]. Finally, pixel-level semantic segmentation is employed to precisely extract pavement crack morphology [15,16]. Input data can vary in format, including grayscale, color, depth, point cloud, and infrared images.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Urban Pavement Distress and Dropped Objects with a Comprehensive Dataset Collected via Smartphone

Xu,

Fu,

et al. 2024

Buildings

Self Cite

View full text Add to dashboard Cite

Pavement distress seriously affects the quality of pavement and reduces driving comfort and safety. The dropped objects from vehicles have increased the risks of traffic accidents. Therefore, automatic detection of urban pavement distress and dropped objects is an effective method to timely evaluate pavement condition. Firstly, this paper utilized a portable platform to collect pavement distress and dropped objects to establish a high-quality dataset. Six types of pavement distresses: transverse crack, longitudinal crack, alligator crack, oblique crack, potholes, and repair, and three types of dropped objects: plastic bottle, metal bottle, and tetra pak were included in this comprehensive dataset. Secondly, the real-time YOLO series detection models were used to classify and localize the pavement distresses and dropped objects. In addition, segmentation models W-segnet, U-Net, and SegNet were utilized to achieve pixel-level detection of pavement distress and dropped objects. The results show that YOLOv8 outperformed YOLOv5 and YOLOv7 with a MAP of 0.889. W-segnet showed an overall MIoU of 70.65% and 68.33% on the training set and test set, respectively, being superior to the comparison model and being able to achieve high-precision pixel-level segmentation. Finally, the trained models were performed on the holdout dataset for the generalization test. The proposed methods integrated the detection of urban pavement distress and dropped objects, which could significantly contribute to driving safety.

show abstract

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Urban Pavement Distress and Dropped Objects with a Comprehensive Dataset Collected via Smartphone

Xu,

Fu,

et al. 2024

Buildings

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, this method is typically used for sampling in the laboratory, which cannot be used for on-site evaluation. The X-ray CT method captures a series of CT images from the field core, which are then processed through digital image processing techniques to reconstruct a 3D numerical model of the asphalt mixture [12,13]. Therefore, the aggregate gradations can be estimated from the virtual asphalt mixture.…”

Section: Introductionmentioning

confidence: 99%

Rapid extraction of pavement aggregate gradation based on point clouds using deep learning networks

Chen

et al. 2023

Automation in Construction

Self Cite

View full text Add to dashboard Cite

Deployment strategies for lightweight pavement defect detection using deep learning and inverse perspective mapping

Yang,

Ma,

Tong

et al. 2024

Automation in Construction

View full text Add to dashboard Cite

Novel Computer Tomography image enhancement deep neural networks for asphalt mixtures

Cited by 8 publications

References 38 publications

Automatic Detection of Urban Pavement Distress and Dropped Objects with a Comprehensive Dataset Collected via Smartphone

Automatic Detection of Urban Pavement Distress and Dropped Objects with a Comprehensive Dataset Collected via Smartphone

Rapid extraction of pavement aggregate gradation based on point clouds using deep learning networks

Deployment strategies for lightweight pavement defect detection using deep learning and inverse perspective mapping

Contact Info

Product

Resources

About