Recognition of void defects in airport runways using ground-penetrating radar and shallow CNN

Zhang, Jun; Lu, Yaming; Zhu, Xin; Zheng, Ting; Liu, Xin; Tian, Yaogang; Li, Weiguang

doi:10.1016/j.autcon.2022.104260

Cited by 34 publications

(10 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Assuming that the sample energy distribution approximately of ( )  x obeys the lognormal distribution. The energy probability density function is shown in Equation (14). Therefore, the expected value  E can be used as the threshold, which can be determined from Equation (15).…”

Section: Reconstructed Energy Spectrummentioning

confidence: 99%

“…Currently, a wide range of methods have been developed for void detection, including impulse response technology [2,3] , acoustic vibration method [4] , distributed optical vibration sensing [5] and ground penetrating radar (GPR) method [6,7] . Among them, GPR is the most effective NDT technology in pavement void detection, and GPR has been widely used in detecting pavement defect [8] , structural thickness [9] , rebar [10,11] , and etc. Through numerical analysis, Li et al found that the length of the void area affects the bearing capacity of the structure, while the void thickness doesn't affect [12] .…”

Section: Introductionmentioning

confidence: 99%

“…To detect the width of the void area, the newly developed deep transfer learning method provide a quick method to locate the void area in the GPR image. For example, our work adopted the hybrid ResNet50-YOLO model to identify the width of moisture damage in asphalt pavements [13] , and used shallow ResNet18-YOLOv2 model with IRS methods can effectively locate the void range in airport runway [14] . Besides, Yan et al used Faster-CNN network to detect pavement crack, and combined the morphological method to calculate the length and width of crack area [15] .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cement pavement void detection algorithm based on GPR signal and continuous wavelet transform method

qiuqin¹,

youxin

tingyi³

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

The dimension of the void area in pavement is crucial to its structural safety. However, there is no effective method to measure its geometric parameters. To address this issue, a void size extraction algorithm based on the continuous wavelet transform (CWT) method was proposed using ground-penetrating radar (GPR) signal. Firstly, the Finite-Difference Time-Domain (FDTD) method was used to investigate void areas with different shapes, sizes, and depths. Next, the GPR signal was processed using the CWT method, and a 3D image of the CWT result was used to visualize the void area. Based on the differences between the void and normal pavement in the time and frequency domains, the signal with maximum energy at the CWT time-frequency result was extracted and combined to reconstruct the B-scan image, where void areas have energy concentration phenomenon, which represent the location of the void area. And width and depth of void detection algorithm was proposed to recognize the energy concentration area. Finally, the detection algorithm was verified both in numerical model and physical lab model. The results indicated that the CWT time-frequency energy spectrum can be used to enhance the void feature, and the 3D CWT image can clearly visualize the void area with a highlighted energy area. After fully testing and validating in numerical and lab models, our proposed method achieved high accuracy for void width and depth extraction, providing a precise method for estimating void dimension in pavement. This method can guide DOT departments to carry out pre-maintenance, thereby ensuring pavement safety.

show abstract

Section: Reconstructed Energy Spectrummentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cement pavement void detection algorithm based on GPR signal and continuous wavelet transform method

qiuqin¹,

youxin

tingyi³

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this subsection, we introduce the application of the YOLO series network in defect detection. In [40], the authors use a YOLOv2-based network to detect void defects in airport runways, combined with incremental random sampling (IRS) and ResNet 18. The localization of hole defects is enhanced, and the recall rate of defect detection is improved.…”

Section: Yolo Target Detection Networkmentioning

confidence: 99%

Detection of Surface Defects on Railway Tracks Based on Deep Learning

Wang

Zhu

et al. 2022

IEEE Access

View full text Add to dashboard Cite

The detection of rail surface defects is very important in railway transportation. However, the edge defects on both sides of the rail and the multi-scale variation between different types of defects both pose challenges to the detection of rail surface defects. In order to solve the above problems, this paper proposes a novel rail surface defect detection network, YOLOv5s-VF. First, we design a sharpening functional attention mechanism (V-CBAM) that contains two key components: adaptive channel attention (F-CAM) and sharpened spatial attention (SSA). In F-CAM, we use one-dimensional convolution with adaptive convolution kernels for cross-channel connections, which reduces the number of parameters of the attention mechanism without affecting its performance. In SSA, we design a sharpening filter suitable for spatial attention, which is used to enhance the attention to the edge position defects of railway tracks and enhance the detection effect of the network on edge defects. Second, we construct a microscale adaptive spatial feature fusion (M-ASFF), which adds a high-resolution feature extraction layer to enhance the details of the underlying features of tiny defects. At the same time, in order to prevent the loss of detailed information and the excessive increase of the parameters of the model, the low-resolution feature layer is removed. Combined with adaptive spatial feature fusion, it can prevent the semantic conflict caused by the fusion of features at different scales. Finally, given the lack of labeled public rail surface defect datasets, this paper is based on the collection of real rail images and manually labels defects to train an object detection network and open source it. The experimental results show that YOLOv5s-VF outperforms the existing rail surface defect detection methods with a detection accuracy of 93.5% and a detection speed of 114.9 fps.

show abstract

“…The exceptional image processing performance of Convolutional Neural Networks (CNNs) has made them a preferred technique for visual recognition tasks, such as image classification [1], object detection [2], style transfer [3], etc. This has motivated their numerous applications across varied industries, for example robotics [4], selfdriving cars [5], medical [6], satellites [7], aviation [8], power systems [9], etc.…”

Section: Introductionmentioning

confidence: 99%

SSP Framework: A New Approach to Designing Lightweight Convolutional Neural Networks

Turay

Vladimirova

2022

IEEE Access

View full text Add to dashboard Cite

show abstract

Recognition of void defects in airport runways using ground-penetrating radar and shallow CNN

Cited by 34 publications

References 35 publications

Cement pavement void detection algorithm based on GPR signal and continuous wavelet transform method

Cement pavement void detection algorithm based on GPR signal and continuous wavelet transform method

Detection of Surface Defects on Railway Tracks Based on Deep Learning

SSP Framework: A New Approach to Designing Lightweight Convolutional Neural Networks

Contact Info

Product

Resources

About