Crack detection based on the mesoscale geometric features for visual concrete bridge inspection

Fan, Yuxin; Zhao, Qi; Shou-dong, NI; Rui, Ting; Ma, Sen; Pang, Na

doi:10.1117/1.jei.27.5.053011

Cited by 6 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Surface defects of underwater dams have typical unstructured characteristics, especially the randomness and diversity of deep-water surface defects [11]. As a non-contact detection method, visual image technology has been used to detect concrete defects [12]. Based on the frequency domain aspect of crack image detection, Medina et al proposed a Gabor filter rotation invariant to process visual images of tunnel surface cracks, whose detection accuracy could be up to 95.27% with the use of optimized filter parameters [13].…”

Section: Related Workmentioning

confidence: 99%

A fine extraction algorithm for image-based surface cracks in underwater dams

Xin¹,

Fan²,

Shi³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

Under the action of water erosion and self-aging action, reservoir dams are prone to have cracks, affecting the safe operation. Underwater visual imaging can be used to detect dam surface cracks, but the spalling, aquatic plants and suspended sediments result in low image contrast and complex backgrounds. With the use of unsupervised machine learning, this paper proposes a fine segmentation and extraction algorithm for image-based dam surface cracks. Firstly, the adaptive histogram equalization is used to make the uneven illumination areas of underwater surface images be even illumination areas, whose statistical characteristics is calculated under the linear spatial filtering. Secondly, the extraction problem of interested crack areas after dodging preprocess is transformed into calculating the distance of image block cluster center, which can distinguish the image blocks of crack features from the background interference features. Thirdly, the fine extraction of crack images is carried out in consideration of the connected domains and morphological features, and the posterior probability of image sample category is obtained based on the soft clustering of Gaussian mixed model. Finally, different extraction algorithms related to surface cracks are evaluated in extensive experiments. The results validate the superior performance of the proposed extraction algorithm with 90.1% extraction accuracy, 6.5% missing alarm rate, and 7.2% false alarm rate.

show abstract

Section: Related Workmentioning

confidence: 99%

A fine extraction algorithm for image-based surface cracks in underwater dams

Xin¹,

Fan²,

Shi³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…It is composed of an offline data processing system and a data acquisition vehicle. If the damping, stiffness, mass and other characteristics of the object structure change, the structural vibration modal quantity can be selected as the weight to weight the modal change parameters before and after the structural damage, so as to realize the identification and effective location of the element damage 4 . An artificial light source is installed on both sides of the detection vehicle to illuminate the image acquisition process to obtain a binary crack detection effect map.…”

Section: Introductionmentioning

confidence: 99%

Design of bridge crack detection algorithm based on wireless sensor network

Li,

Sun,

et al. 2023

Third International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2023)

View full text Add to dashboard Cite

The quality and safety of bridges are related to the national economy and people's livelihood, and to thousands of families. The reason for bridge collapse is often due to the lack of scientific and timely bridge disease detection. However, when the bridge is completed and put into use, it will be repeatedly crushed by external forces and affected by natural factors such as rain, snow, flood, freezing and earthquake. As one of the most important bridge diseases, cracks seriously affect the safe operation of bridges, and even lead to accidents of bridge destruction and death. In view of this situation, this paper studies the bridge crack detection based on wireless sensor network. By setting appropriate threshold, the background and target are divided and the relevant feature information of cracks is extracted. It is composed of an off-line data processing system and a data acquisition vehicle. Finally, through the evaluation and comparison, the favorable rate of this algorithm is higher than the other two algorithms, and the favorable rate will be as high as 88.42%. In the test of running time, the running time of this algorithm is shorter than that of the other two algorithms, and the average running time is only 6.02 seconds. It can be seen that this algorithm is more suitable for bridge crack detection.

show abstract

“…In addition, these methods are prone to errors due to environmental changes such as lighting conditions, and crack-like features such as concrete joints are usually falsely detected as cracks. In the past few years, machine learning-based methods have become popular for detecting cracks, e.g., random forest [15] and support vector machine [16]. What mostly revolutionized this area, however, is the Convolutional Neural Network (CNN) aproach [6,9,[17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation and 3D Reconstruction of Concrete Cracks

2022

View full text Add to dashboard Cite

Damage assessment of concrete structures is necessary to prevent disasters and ensure the safety of infrastructure such as buildings, sidewalks, dams, and bridges. Cracks are among the most prominent damage types in such structures. In this paper, a solution is proposed for identifying and modeling cracks in concrete structures using a stereo camera. First, crack pixels are identified using deep learning-based semantic segmentation networks trained on a custom dataset. Various techniques for improving the accuracy of these networks are implemented and evaluated. Second, modifications are applied to the stereo camera’s calibration model to ensure accurate estimation of the systematic errors and the orientations of the cameras. Finally, two 3D reconstruction methods are proposed, one of which is based on detecting the dominant structural plane surrounding the crack, while the second method focuses on stereo inference. The experiments performed on close-range images of complex and challenging scenes show that structural cracks can be identified with a precision of 96% and recall of 85%. In addition, an accurate 3D replica of cracks can be produced with an accuracy higher than 1 mm, from which the cracks’ size and other geometric features can be deduced.

show abstract

Crack detection based on the mesoscale geometric features for visual concrete bridge inspection

Cited by 6 publications

References 22 publications

A fine extraction algorithm for image-based surface cracks in underwater dams

A fine extraction algorithm for image-based surface cracks in underwater dams

Design of bridge crack detection algorithm based on wireless sensor network

Semantic Segmentation and 3D Reconstruction of Concrete Cracks

Contact Info

Product

Resources

About