Unified Approach to Pavement Crack and Sealed Crack Detection Using Preclassification Based on Transfer Learning

Zhang, Kaige; Cheng, Heng-Da; Zhang, Boyu

doi:10.1061/(asce)cp.1943-5487.0000736

Cited by 189 publications

(97 citation statements)

References 38 publications

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“…Though some studies do classify the damage based on types-for example, Zalama et al (2014) classified damage types vertically and horizontally, and Akarsu et al (2016) categorized damage into three types, namely, vertical, horizontal, and crocodile-most studies primarily focus on classifying damages between a few types. There are other studies that detect blurry road markings (Kawano et al, 2017), and classify the cracks and sealed cracks (Zhang et al, 2018). Therefore, for a practical damage detection model for use by municipalities, it is necessary to clearly distinguish and detect different types of road damage; this is because, depending on the type of damage, the road administrator needs to follow different approaches to rectify the damage.…”

Section: Road Damage Detection Using Image Processingmentioning

confidence: 99%

Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images

Maeda

Sekimoto

Seto

et al. 2018

Computer aided Civil Eng

602

338

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Research on damage detection of road surfaces using image processing techniques has been actively conducted. This study makes three contributions to address road damage detection issues. First, to the best of our knowledge, for the first time, a large‐scale road damage data set is prepared, comprising 9,053 road damage images captured using a smartphone installed on a car, with 15,435 instances of road surface damage included in these road images. Next, we used state‐of‐the‐art object detection methods using convolutional neural networks to train the damage detection model with our data set, and compared the accuracy and runtime speed on both, using a GPU server and a smartphone. Finally, we demonstrate that the type of damage can be classified into eight types with high accuracy by applying the proposed object detection method. The road damage data set, our experimental results, and the developed smartphone application used in this study are publicly available (https://github.com/sekilab/RoadDamageDetector/).

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Section: Road Damage Detection Using Image Processingmentioning

confidence: 99%

Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images

Maeda

Sekimoto

Seto

et al. 2018

Computer aided Civil Eng

602

338

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“…Convolutional neural networks (CNNs) have been highlighted in many image‐based problems. An extensive number of SHM applications of CNNs focus on vision‐based surface defect detection and image recognition for construction safety . Intuitively, CNNs imitate the sensing functions of the animal visual cortex (individual cortical neurons respond to stimuli only in a restricted region of the visual field) by gathering information from neighboring inputs to form subfeatures in the filters.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring

Tang

Chen

Bao

et al. 2018

Struct Control Health Monit

270

156

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Structural health monitoring (SHM) is used worldwide for managing and maintaining civil infrastructures. SHM systems have produced huge amounts of data, but the effective monitoring, mining, and utilization of this data still need in-depth study. SHM data generally includes multiple types of anomalies caused by sensor faults or system malfunctions that can disturb structural analysis and assessment. In the routine data pre-processing, multiple signal processing techniques are required to detect the anomalies, respectively, which is inefficient. The large variations of extracted features from massive SHM data make the data anomaly detection techniques prone to be over-processed or under-processed. Even with expert intervention, the parameter tuning, associated with multiple data preprocessing methods, is still a challenge, making the procedure expensive and inefficient. In addition, one data anomaly detection technique frequently mis-detects other types of anomaly. In this work, we focus on the anomaly detection in the stage of data pre-processing that little work has been done based on the real-world continuous SHM data with multiclass anomalies. We proposed a novel data anomaly detection method based on a convolutional neural network (CNN) that imitates human vision and decision making. First, we split raw time series data into sections, and visualized the data in time and frequency domain, respectively. Then each section's images were stacked as a single dual-channel image and labeled according to graphical features (multi-2D image space expression). Second, a CNN was designed and trained for data anomaly classification, during which the descriptions and representations of the anomalies' features were learned by convolution. To validate our work, we considered the effects of balanced and imbalanced training sets and training ratios on actual acceleration data from an SHM system for a long-span cable-stayed bridge. The results show that our method could detect the multipattern anomalies of SHM data efficiently with high accuracy. The proposed dual-information CNN-based design makes this detection process readily scalable, faster, and more accurate, thereby providing a novel perspective with strong potential for SHM data preprocessing. KEYWORDScomputer vision, convolutional neural network (CNN), data anomaly detection, long-span bridge, structural health monitoring (SHM) | INTRODUCTIONStructural health monitoring (SHM) is used worldwide to manage and maintain civil infrastructure systems by evaluating their structural loads, responses, and real-time performance, and by predicting the future behavior of structures of all types. 1-4 SHM has produced huge amounts of data. For example, the SHM system used for the Sutong Bridge in China, which has 785 sensors, produces 2.5 TB of data each year. The effective mining and utilization of SHM data is an important topic that needs in-depth study. However, researchers have faced challenges because the SHM data generally include multiple types of anomalies caused by sensor fa...

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“…Based on a recent statistics done by the Central Intelligence Agency, the total length of road networks in the world has amounted to 64,285,009 km; such length of roads demands an enormous cost for maintenance and upgrading tasks [2]. In Vietnam, according to the report of the General Statistics Office of Vietnam in 2010, the total length of asphalted roads has reached 93,535 km [3].…”

Section: Introductionmentioning

confidence: 99%

Fast Local Laplacian‐Based Steerable and Sobel Filters Integrated with Adaptive Boosting Classification Tree for Automatic Recognition of Asphalt Pavement Cracks

Hoang

Nguyen

2018

Advances in Civil Engineering

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Effective road maintenance requires adequate periodic surveys of asphalt pavement condition. The manual process of pavement assessment is labor intensive and time-consuming. This study proposes an alternative for automating the periodic surveys of pavement condition by means of image processing and machine learning. Advanced image processing techniques including fast local Laplacian filter, Sobel filter, steerable filter, and projection integral are employed for image enhancement and analysis to extract useful features from digital images. Based on the features produced by these image processing techniques, adaptive boosting classification tree is used to perform pavement crack recognition tasks. A dataset of image samples consisting of five classes (alligator crack, diagonal crack, longitudinal crack, noncrack, and transverse crack) has been collected to construct and verify the performance of the adaptive boosting classification tree. The experimental results show that the proposed approach has achieved a high crack classification accuracy which is roughly 90%. Therefore, the newly developed model is a promising alternative to help transportation agencies in pavement condition evaluation.

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Unified Approach to Pavement Crack and Sealed Crack Detection Using Preclassification Based on Transfer Learning

Cited by 189 publications

References 38 publications

Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images

Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images

Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring

Fast Local Laplacian‐Based Steerable and Sobel Filters Integrated with Adaptive Boosting Classification Tree for Automatic Recognition of Asphalt Pavement Cracks

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