An Advanced Otsu Method Integrated with Edge Detection and Decision Tree for Crack Detection in Highway Transportation Infrastructure

Han, Haihang; Deng, Han‐Yu; Dong, Qiao; Gu, Xingyu; Zhang, Tianjie; Wang, Yangyang

doi:10.1155/2021/9205509

Cited by 32 publications

(18 citation statements)

References 21 publications

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“…Han et al [48] proposed an advanced Otsu method integrated with edge detector and tree-based classifier for crack detection in highway; Gaussian function-based spatial filtering and top-hat transform are also employed for image enhancement and performances of various edge detectors including Prewitt, Sobel, Gauss-Laplace (LoG), and Canny are assessed; the research finding is that the tree-based classifier is capable of recognizing crack patterns effectively. Ranjbar et al [15] relies on transfer learning used with pretrained deep neural network models for pavement crack detection; however, the proposed method has not considered sealed cracks as objects of interest.…”

Section: Related Workmentioning

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: Related Workmentioning

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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“…Crack segmentation methods based on machine vision are mainly divided into traditional algorithms [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ] and deep-learning-based methods [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

A Crack Segmentation Model Combining Morphological Network and Multiple Loss Mechanism

Zhao

Chao

2023

Sensors

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With the wide application of computer vision technology and deep-learning theory in engineering, the image-based detection of cracks in structures such as pipelines, pavements and dams has received more and more attention. Aiming at the problems of high cost, low efficiency and poor detection accuracy in traditional crack detection methods, this paper proposes a crack segmentation network by combining a morphological network and a multiple-loss mechanism. First, for improving the identification of cracks with different resolutions, the U-Net network is used to extract multi-scale features from the crack image. Second, for eliminating the effect of polarized light on the cracks under different illuminations, the extracted crack features are further morphologically processed by a white-top hat transform and a black-bottom hat transform. Finally, a multi-loss mechanism is designed to solve the problem of the inaccurate segmentation of cracks on a single scale. Extensive experiments are carried out on five open crack datasets: Crack500, CrackTree200, CFD, AEL and GAPs384. The experimental results showed that the average ODS, OIS, AIU, sODS and sOIS are 75.7%, 73.9%, 36.4%, 52.4% and 52.2%, respectively. Compared with state-of-the-art methods, the proposed method achieves better crack segmentation performance. Ablation experiments also verified the effectiveness of each module in the algorithm.

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“…Defects such as cracks, ruts, potholes, and subsidence are prone to occur during the period of pavement service. Among them, cracks, as the most important pavement disease, should be paid more attention [1][2][3][4]. Cracks can not only affect the appearance of the pavement but also have a great impact on the performance of the pavement if not treated in time [5].…”

Section: Introductionmentioning

confidence: 99%

Effects of Dioctyl Phthalate on Performance of Asphalt Sealant

Luo¹,

Guo

Wang

et al. 2022

Advances in Materials Science and Engineering

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Dioctyl phthalate (DOP) was introduced into asphalt-based sealant as a plasticizer in this work. Four kinds of sealants with different DOP content were prepared, and thermal stability and low-temperature performance of five samples with asphalt as a control group were evaluated by fluidity test, softening point test, thermogravimetric-differential thermal analysis (TG-DTA), low-temperature tensile test, and differential scanning calorimeter (DSC), respectively. Furthermore, the mechanism of DOP on sealant properties was analyzed by Fourier transform infrared spectroscopy (FTIR) and fluorescence microscope. Results show that DOP can significantly improve the low-temperature performance of sealant, but slightly deteriorate the high-temperature performance. This is because DOP can supplement the light component of sealant and enhance the fluidity of the internal components. On the one hand, DOP reduces the apparent activation energy of sealant and lowers the barrier to chemical reactions, indicating that DOP is slightly detrimental to the thermal stability of sealant. On the other hand, DOP greatly reduces the glass transition temperature of sealant and improves its low-temperature performance. However, there is no evident chemical reaction between DOP and sealant, and physical effects play a major role in the effects of DOP on sealant properties. In addition, DOP can promote the expansion and cross-linking of the polymer in the sealant and ultimately enhance the compatibility between asphalt and polymer molecules. In short, DOP is recommended as a plasticizer to prepare asphalt sealants with excellent properties.

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An Advanced Otsu Method Integrated with Edge Detection and Decision Tree for Crack Detection in Highway Transportation Infrastructure

Cited by 32 publications

References 21 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

A Crack Segmentation Model Combining Morphological Network and Multiple Loss Mechanism

Effects of Dioctyl Phthalate on Performance of Asphalt Sealant

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