Pavement Defects Detection and Classification Using Smartphone-Based Vibration and Video Signals

Christodoulou, Symeon E.; Hadjidemetriou, Georgios M.; Kyriakou, Charalambos

doi:10.1007/978-3-319-91635-4_7

Cited by 8 publications

(8 citation statements)

References 18 publications

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“…[8][9][10] In the field of structural condition assessment, many deep learning-based techniques have been proposed as an attempt to increase the level of automation of condition inspection, which image analysis-based methods failed to achieve. [11][12][13][14] This includes applications for the detection of defects in infrastructural assets such as cracks in road surfaces, [15][16][17][18][19] bridges 20 and in water and sewerage pipelines. 14,21 Another research field on object detection that also received considerable attention amongst researchers in recent years is the development of deep learning algorithms for post-disaster structural condition identification.…”

Section: Related Workmentioning

confidence: 99%

“…25 As a result, several mobile applications have been proposed as practical solutions to many problems: for example, a machine learning (ML) application running on smartphones that farmers can use to detect early-stage disease in plants, 26 detection and classification of coffee leaves 27 and pavement defects detection. 18 In the latter application, the authors developed a smartphone-based method for detecting roadway defects through image signal streams. The developed detector that uses a support vector machine (SVM) classifier is trained and tested on vectors of features generated from the input images histograms in addition to another two texture descriptors of non-overlapped square blocks that includes 'patch' and 'no-patch' areas.…”

Section: Related Workmentioning

confidence: 99%

“…The developed detector that uses a support vector machine (SVM) classifier is trained and tested on vectors of features generated from the input images histograms in addition to another two texture descriptors of non-overlapped square blocks that includes 'patch' and 'no-patch' areas. 18,19 In another similar application, also on detecting road defects, 16 the authors use images captured from a smartphone mounted on a vehicle to detect eight different types of road surface damages. The authors developed a custom, large-scale dataset comprising more than 9000 images containing 15,400 instances of road surface damages to build their detector.…”

Section: Related Workmentioning

confidence: 99%

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Deep learning smartphone application for real‐time detection of defects in buildings

Perez

Tah

2021

Struct Control Health Monit

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Condition assessment and health monitoring (CAHM) of built assets requires effective and continuous monitoring of any changes to the material and/or geometric properties of the assets in order to detect any early signs of defects or damage and act on time. Most of the traditional CAHM techniques, however, depend on manual labour despite that, in some cases, the inspection environment can be unsafe and could lead to low efficiency or misjudgement of the severity of the defect. In recent years, computer vision techniques have been proposed as an automated alternative to the traditional CAHM techniques as methods for extracting and analysing feature-related information from asset images and videos. Such methods have proven to be robust and effective solutions, complementary to current time-consuming and unreliable manual observational practices. This work is concerned with the development of a deep learning-based smartphone app, which allows real-time detection of four types of defects in buildings, namely, cracks, mould, stain and paint deterioration. Since smartphones are widely available and equipped with highresolution cameras, this application can offer a practical, low-cost solution for condition assessment procedures of built assets. The obtained results are promising and support the feasibility and effectiveness of the approach to identify and classify various types of building defects. K E Y W O R D Scondition assessment and health monitoring, deep learning, defects in buildings, object detection, smartphone applications | INTRODUCTIONDefects are defined as the deterioration of building features and services to unsatisfactory quality levels of the requirements of the users. 1 Defects and deterioration are common problems in built structures; they are a major concern of buildings and require significant attention. Although various defects are more common in old buildings, they can occur in new buildings as well. 2 Workers' faults, natural causes, poor maintenance and the age of the building are amongst the common causes of defects in built structures. 3 There are many types of building defects. In general, these can be divided into two categories: structural and non-structural defects. Structural defects are those which occur in a

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Deep learning smartphone application for real‐time detection of defects in buildings

Perez

Tah

2021

Struct Control Health Monit

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“…Texture information belongs to image information, which is used to represent the roughness or irregular information. Entropy, as a statistical measure of randomness, is a measure of the information content of an image [43]. erefore, an entropy filter is chosen to calculate the texture information and represents the nonuniformity or complexity of the texture in the pothole.…”

Section: Pothole Segmentation Principle and Processmentioning

confidence: 99%

Detection and Segmentation of Cement Concrete Pavement Pothole Based on Image Processing Technology

Gao

Wang

Zhu

et al. 2020

Mathematical Problems in Engineering

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Potholes are the most common form of distress on cement concrete pavements, which can compromise pavement safety and ridability. Thus, timely and accurate pothole detection is an important task in developing proper maintenance strategies and ensuring driving safety. This paper proposes a method of integrating the processing of grayscale and texture features. This method mainly combines industrial camera to realize rapid and accurate detection of pothole. Image processing techniques including texture filters, image grayscale, morphology, and extraction of the maximum connected domain are used synergistically to extract useful features from digital images. A machine learning model based on the library for support vector machine (LIBSVM) is constructed to distinguish potholes from longitudinal cracks, transverse cracks, and complex cracks. The method is validated using data collected from agricultural and pastoral areas of Inner Mongolia, China. The comprehensive experiments for recognition of potholes show that the recall, precision, and F1-Score achieved are 100%, 97.4%, and 98.7%, respectively. In addition, the overlap rate between the extracted pothole region and the original image is estimated. Images with an overlap rate greater than 90% accounted for 76.8% of the total image, and images with an overlap rate greater than 80% accounted for 94% of the total image. A comparison discloses that the proposed approach is superior to the existing method not only from the perspective of the accuracy of pothole detection but also from the perspective of the segmentation effect and processing efficiency.

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“…In practice, management and planning of transport infrastructure assets is conducted in different ways by decision-makers, who follow their organisations' guidelines and attempt to effectively use their available budget (Hadjidemetriou et al, 2020a). Assets are assessed either manually by inspectors or automatically with the aid of sensors and novel monitoring technologies, which can be based on computer vision and artificial intelligence (Christodoulou et al, 2018;Hadjidemetriou et al, 2015;Zhu et al, 2020). Asset monitoring along their lifetime facilitates the development of predictive models, which in turn assist the development of maintenance prioritisation strategies (Dhada et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Road Network Layout Planning based on Evaluation of Connectivity and Asset Criticality

Hadjidemetriou¹,

Herrera²,

Parlikad³

2021

Proceedings of the 2021 European Conference on Computing in Construction

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State of the art research in ensuring transport infrastructure resilience focuses on adopting a network perspective. However, there is no comprehensive, widespread method for evaluating connectivity and proposing alternative routes to improve it. Presented herein is a framework that: assesses road network connectivity, using a "closeness" measure and focusing on routes passing by the vulnerable asset of bridges; and proposes the development of optimal alternative routes, using a genetic algorithm. The results showed a significant improvement of network connectivity and the potential of the method to serve as the basis for updated transport infrastructure planning practices.

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Pavement Defects Detection and Classification Using Smartphone-Based Vibration and Video Signals

Cited by 8 publications

References 18 publications

Deep learning smartphone application for real‐time detection of defects in buildings

Deep learning smartphone application for real‐time detection of defects in buildings

Detection and Segmentation of Cement Concrete Pavement Pothole Based on Image Processing Technology

Road Network Layout Planning based on Evaluation of Connectivity and Asset Criticality

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