Application of image technology on pavement distress detection: A review

Du, Zhenyu; Yuan, Jie; Xiao, Feipeng; Hettiarachchi, Chamod

doi:10.1016/j.measurement.2021.109900

Cited by 72 publications

(19 citation statements)

References 114 publications

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“…e subsequent parts of this section review the image texture descriptors' use for feature extraction, the machine learning, and the metaheuristic algorithm employed for model optimization. Since crack pixels generally contrasts with those of pavement surface and pavement images are often perturbed by complex background and noise [17,54], this work relies on the methods of Gaussian steerable filters and projection integrals to isolate crack objects and analyze their visual appearance.…”

Section: Methodsmentioning

confidence: 99%

A Novel Approach for Detection of Pavement Crack and Sealed Crack Using Image Processing and Salp Swarm Algorithm Optimized Machine Learning

Hoang

Huynh

Tran

et al. 2022

Advances in Civil Engineering

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During the phase of periodic survey, sealed crack and crack in asphalt pavement surface should be detected accurately. Moreover, the capability of identifying these two defects can help reduce the false-positive rate for pavement crack detection. Because crack and sealed crack are both line-based defects and may resemble each other in shape, this study puts forward an innovative method based on computer vision for detecting sealed crack and crack. This method is an integration of feature extraction based on image processing and metaheuristic optimized machine learning. Image processing is used to compute features that characterize visual appearance and texture of the pavement image. Subsequently, Salp Swarm Algorithm integrated with multiclass support vector machine is employed for pattern recognition. Based on experimental results, the newly developed method has achieved the most desired predictive performance with an accuracy rate = 91.33% for crack detection and 92.83% for sealed crack detection.

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Section: Methodsmentioning

confidence: 99%

A Novel Approach for Detection of Pavement Crack and Sealed Crack Using Image Processing and Salp Swarm Algorithm Optimized Machine Learning

Hoang

Huynh

Tran

et al. 2022

Advances in Civil Engineering

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“…Thermography measures temperature quickly enough to meet the purpose of real-time detection. Also, the Infrared ray (IR) is a band of invisible light found on the electromagnetic spectrum, with wavelengths ranging from 0.75 to 100 [45]. Thermal infrared cameras can detect infrared radiation from the short-wave to the long-wave region, wherein the infrared detector receives the infrared radiation energy distribution of the target, the sensor converts the radiation into an electrical signal, and the output gets processed to form an infrared thermal image.…”

Section: Difference Between Infrared Thermal Imaging and Optical Imag...mentioning

confidence: 99%

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

et al. 2021

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Automatic damage detection using deep learning warrants an extensive data source that captures complex pavement conditions. This paper proposes a thermal-RGB fusion image-based pavement damage detection model, wherein the fused RGB-thermal image is formed through multi-source sensor information to achieve fast and accurate defect detection including complex pavement conditions. The proposed method uses pre-trained EfficientNet B4 as the backbone architecture and generates an argument dataset (containing non-uniform illumination, camera noise, and scales of thermal images too) to achieve high pavement damage detection accuracy. This paper tests separately the performance of different input data (RGB, thermal, MSX, and fused image) to test the influence of input data and network on the detection results. The results proved that the fused image’s damage detection accuracy can be as high as 98.34% and by using the dataset after augmentation, the detection model deems to be more stable to achieve 98.35% precision, 98.34% recall, and 98.34% F1-score.

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“…However, they also include a majority of the non-DL based ML methods. Whilst some other works like in [8], [21], [22], [24], [28], [29] include sections about DL methods for surface cracks, it is not a major focus, therefore, leaving out specific details about the different DL based approaches. Some works focus exclusively on surface cracks and DL [23], [25], [30].…”

Section: Introductionmentioning

confidence: 99%

What's Cracking? A Review and Analysis of Deep Learning Methods for Structural Crack Segmentation, Detection and Quantification

König¹,

Jenkins²,

Mannion³

et al. 2022

Preprint

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Surface cracks are a very common indicator of potential structural faults. Their early detection and monitoring is an important factor in structural health monitoring. Left untreated, they can grow in size over time and require expensive repairs or maintenance. With recent advances in computer vision and deep learning algorithms, the automatic detection and segmentation of cracks for this monitoring process have become a major topic of interest. This review aims to give researchers an overview of the published work within the field of crack analysis algorithms that make use of deep learning. It outlines the various tasks that are solved through applying computer vision algorithms to surface cracks in a structural health monitoring setting and also provides in-depth reviews of recent fully, semi and unsupervised approaches that perform crack classification, detection, segmentation and quantification. Additionally, this review also highlights popular datasets used for cracks and the metrics that are used to evaluate the performance of those algorithms. Finally, potential research gaps are outlined and further research directions are provided.

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Application of image technology on pavement distress detection: A review

Cited by 72 publications

References 114 publications

A Novel Approach for Detection of Pavement Crack and Sealed Crack Using Image Processing and Salp Swarm Algorithm Optimized Machine Learning

A Novel Approach for Detection of Pavement Crack and Sealed Crack Using Image Processing and Salp Swarm Algorithm Optimized Machine Learning

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

What's Cracking? A Review and Analysis of Deep Learning Methods for Structural Crack Segmentation, Detection and Quantification

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