A novel pavement transverse cracks detection model using WT-CNN and STFT-CNN for smartphone data analysis

Abstract

This paper proposes a novel pavement transverse crack detection model based on time-frequency analysis and convolutional neural networks. The accelerometer and smartphone installed in the vehicle collect the vibration response between the wheel and the road, such as pavement transverse cracks, manholes, and normal pavement. Since the original vibration signal can only contain a onedimensional domain (time-acceleration). Time-frequency analysis, including Short-Time Fourier Transform and Wavelet Transform, can transfer the one-dimensional vibration signal into a twodimensional time-frequency-energy spectrum matrix. The energy spectrum matrix obtained from STFT and WT can effectively obtain different signal features in terms of time and frequency features. If STFT and WT are further combined with CNN models, STFT-CNN and WT-CNN, respectively, pavement transverse cracks can be detected more accurately. In this study, the reliability of the developed pavement transverse cracks detection model was evaluated based on the data collected by conducting a road driving test. Analysis results of the developed model show that the accuracies of WT-CNN and STFT-CNN are 97.2% and 91.4%, respectively. The F1 scores to analyse the practicability and the adaptability of the crack detection model of WT-CNN and STFT-CNN are 96.35% and 89.56%, respectively.

Deep Learning-Based Thermal Image Analysis for Pavement Defect Detection and Classification Considering Complex Pavement Conditions

“…Data Type Accuracy (%) Zhang et al [47] RGB image (Kinect-Based) 89.09 Zhou and Song [22] laser-scanned range images 99.6 (average) Zhong et al [9] 3D laser scanning Over 98 Chen et.al. [6] vibration signal 97.2 Our method RGB image with thermal information 98.34%…”

“…Similar to works proposed by [6,41], the performance of our proposed models were evaluated using metrics like accuracy, precision, recall, and F1 score based on their respective true positive (TP), false positive (FP), true negative (TN) and false negative (FN) values. These metrics were computed as follows:…”

“…However, for the actual complex road conditions, the existing methods have limited error detection rates to identify all kinds of cases [5]. Multi-sensor fusion processing idea for complex road conditions was considered where acceleration sensors [6], infrared sensors [7], multi-vision cameras [8], and 3D laser scanning [9] can provide additional identification information to the optical images of the pavement.…”

“…Majidifard et al [21] combined YOLO (you only look once) deep learning framework and U-net as a two-step network to identify and extract cracks from the pavement image and made a detailed subdivision into eight types of cracks and potholes for the complex scenes of the pavement. Also, Chen et al [6] also used a fusion network with acceleration using wavelet transform and VGG16 to identify and detect transverse cracks and manholes.…”

Crack Detection in Frozen Soils Using Infrared Thermographic Camera

“…Afshani, et al [ 13 ] conducted a study to detect defects in the lining of a tunnel with an infrared thermal camera. Recently, deep learning and machine learning analyses were applied to find a crack in infrastructures [ 14 , 15 ]. Moreover, the three-dimensional monitoring system was applied for monitoring the displacement and tilt of infrastructure using laser scanning [ 16 ].…”