Safety-critical infrastructures, such as bridges, are periodically inspected to check for existing damage, such as fatigue cracks and corrosion, and to guarantee the safe use of the infrastructure. Visual inspection is the most frequent type of general inspection, despite the fact that its detection capability is rather limited, especially for fatigue cracks. Machine learning algorithms can be used for augmenting the capability of classical visual inspection of bridge structures, however, the implementation of such an algorithm requires a massive annotated training dataset, which is time-consuming to produce. This paper proposes a semi-automatic crack segmentation tool that eases the manual segmentation of cracks on images needed to create a training dataset for a machine learning algorithm. Also, it can be used to measure the geometry of the crack. This tool makes use of an image processing algorithm, which was initially developed for the analysis of vascular systems on retinal images. The algorithm relies on a multi-orientation wavelet transform, which is applied to the image to construct the so-called "orientation scores", i.e. a modified version of the image. Afterwards, the filtered orientation scores are used to formulate an optimal path problem that identifies the crack. The globally optimal path between manually selected crack endpoints is computed, using a state-of-the-art geometric tracking method. The pixel-wise segmentation is done afterwards using the obtained crack path. The proposed method outperforms fully automatic methods and shows potential to be an adequate alternative to the manual data annotation.
Indian Institute of Technology, Madras（印度理工学院马德拉斯分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Rizzo International, Inc.（里佐国际公司）
The detection of cracks is a crucial task in monitoring structural health and ensuring structural safety. The manual process of crack detection is time-consuming and subjective to the inspectors. Several researchers have tried tackling this problem using traditional Image Processing or learning-based techniques. However, their scope of work is limited to detecting cracks on a single type of surface (walls, pavements, glass, etc.). The metrics used to evaluate these methods are also varied across the literature, making it challenging to compare techniques. This paper addresses these problems by…
EfficientCrackNet: A Lightweight Model for Crack Segmentation
Aligarh Muslim University（阿里格尔穆斯林大学）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Chengdu University（成都大学）；Monash University Malaysia（莫纳什大学马来西亚分校）；Tohoku University（东北大学）
Staircase Cascaded Fusion of Lightweight Local Pattern Recognition and Long-Range Dependencies for Structural Crack Segmentation
Engineering Research Center of Learning-Based Intelligent System (Ministry of Education)（基于学习的智能系统工程研究中心（教育部））；Key Laboratory of Computer Vision（计算机视觉重点实验室）；Tianjin Key Laboratory of Civil Structure Protection（天津建筑物结构保护重点实验室）；Tianjin Chengjian University（天津城建大学）
Fast Measuring Pavement Crack Width by Cascading Principal Component Analysis
Faculty of Information Technology, Beijing University of Technology, No.100 Pingleyuan Road, Chaoyang District, Beijing 100124, China（信息技术学院，北京理工大学，北京市朝阳区平乐园路100号，北京100124，中国）
University of Ljubljana（卢布尔雅那大学）；Kolektor Group d. o. o.（Kolektor集团有限公司）；Kolektor Orodjarna d. o. o.（Kolektor Orodjarna有限公司）
Automated surface-anomaly detection using machine learning has become an interesting and promising area of research, with a very high and direct impact on the application domain of visual inspection. Deep-learning methods have become the most suitable approaches for this task. They allow the inspection system to learn to detect the surface anomaly by simply showing it a number of exemplar images. This paper presents a segmentation-based deep-learning architecture that is designed for the detection and segmentation of surface anomalies and is demonstrated on a specific domain of surface-crack d…
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. I…
Surface crack segmentation poses a challenging computer vision task as background, shape, colour and size of cracks vary. In this work we propose optimized deep encoder-decoder methods consisting of a combination of techniques which yield an increase in crack segmentation performance. Specifically we propose a decoder-part for an encoder-decoder based deep learning architecture for semantic segmentation and study its components to achieve increased performance. We also examine the use of different encoder strategies and introduce a data augmentation policy to increase the amount of available t…
Topology-aware Mamba for Crack Segmentation in Structures
School of Computer Science and Engineering, Jiangsu University of Science and Technology（江苏科技大学计算机科学与工程学院）；School of Electronic and Informatics Engineering, Jiangsu University（江苏大学电子与信息工程学院）；School of Civil and Environmental Engineering, Oklahoma State University（俄克拉荷马州立大学土木与环境工程学院）
SenseTime（商汤科技）；School of Electronic and Electrical Engineering, Shanghai University of Engineering Science（上海工程技术大学电子与电气工程学院）；School of Computer Science and Technology, Donghua University（东华大学计算机科学与技术学院）
CrackSegDiff: Diffusion Probability Model-based Multi-modal Crack Segmentation
School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；School of Electronic and Electrical Engineering, Shanghai University of Engineering Science（上海工程技术大学电子与电气工程学院）；SenseTime（商汤科技）
Automated pavement crack image segmentation is challenging because of inherent irregular patterns, lighting conditions, and noise in images. Conventional approaches require a substantial amount of feature engineering to differentiate crack regions from non-affected regions. In this paper, we propose a deep learning technique based on a convolutional neural network to perform segmentation tasks on pavement crack images. Our approach requires minimal feature engineering compared to other machine learning techniques. We propose a U-Net-based network architecture in which we replace the encoder wi…
CrackSegFlow: Controllable Flow Matching Synthesis for Generalizable Crack Segmentation with a 50K Image-Mask Benchmark
Department of Civil and Environmental Engineering, Grainger College of Engineering, University of Illinois Urbana--Champaign（土木与环境工程系，格拉inger工程学院，伊利诺伊大学厄巴纳-香槟分校）；Department of Computer Science, Grainger College of Engineering, University of Illinois Urbana--Champaign（计算机科学系，格拉inger工程学院，伊利诺伊大学厄巴纳-香槟分校）
Friedrich-Alexander University Erlangen-Nuremberg（弗里德里希-亚历山大-埃尔兰根-纽伦堡大学）
Photovoltaic is one of the most important renewable energy sources for dealing with world-wide steadily increasing energy consumption. This raises the demand for fast and scalable automatic quality management during production and operation. However, the detection and segmentation of cracks on electroluminescence (EL) images of mono- or polycrystalline solar modules is a challenging task. In this work, we propose a weakly supervised learning strategy that only uses image-level annotations to obtain a method that is capable of segmenting cracks on EL images of solar cells. We use a modified Res…
Fine-tuning vision foundation model for crack segmentation in civil infrastructures
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Department of Civil Engineering, Tsinghua University（清华大学土木工程系）；School of Civil and Resource Engineering, University of Science and Technology Beijing（北京科技大学土木与资源工程学院）
Pixel-level crack segmentation is widely studied due to its high impact on building and road inspections. While recent studies have made significant improvements in accuracy, they typically heavily depend on pixel-level crack annotations, which are time-consuming to obtain. In earlier work, we proposed to reduce the annotation cost bottleneck by reformulating the crack segmentation problem as a weakly-supervised problem -- i.e. the annotation process is expedited by sacrificing the annotation quality. The loss in annotation quality was remedied by refining the inference with per-pixel brightne…
Anomaly detection and localization is an important vision problem, having multiple applications. Effective and generic semantic segmentation of anomalous regions on various different surfaces, where most anomalous regions inherently do not have any obvious pattern, is still under active research. Periodic health monitoring and fault (anomaly) detection in vast infrastructures, which is an important safety-related task, is one such application area of vision-based anomaly segmentation. However, the task is quite challenging due to large variations in surface faults, texture-less construction ma…
FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM
School of Electronic and Electronics Engineering, Shanghai University of Engineering Science（上海工程技术大学电子与电气工程学院）；Leiden University（莱顿大学）；School of Information Technology, Murdoch University（默多克大学信息技术学院）；University of Washington（华盛顿大学）；Leiden Institute of Advanced Computer Science, Leiden University（莱顿大学高级计算机科学研究所）
The Chinese University of Hong Kong（香港中文大学）；Minjiang University（闽江学院）
Automatic crack detection and segmentation play a significant role in the whole system of unmanned aerial vehicle inspections. In this paper, we have implemented a deep learning framework for crack detection based on classical network architectures including Alexnet, VGG, and Resnet. Moreover, inspired by the feature pyramid network architecture, a hierarchical convolutional neural network (CNN) deep learning framework which is efficient in crack segmentation is also proposed, and its performance of it is compared with other state-of-the-art network architecture. We have summarized the existin…
Technische Universität Dresden（德累斯顿工业大学）；Zhejiang University（浙江大学）
Structures suffer from the emergence of cracks, therefore, crack detection is always an issue with much concern in structural health monitoring. Along with the rapid progress of deep learning technology, image semantic segmentation, an active research field, offers another solution, which is more effective and intelligent, to crack detection Through numerous artificial neural networks have been developed to address the preceding issue, corresponding explorations are never stopped improving the quality of crack detection. This paper presents a novel artificial neural network architecture named …
Many loss functions have been derived from cross-entropy loss functions such as large-margin softmax loss and focal loss. The large-margin softmax loss makes the classification more rigorous and prevents overfitting. The focal loss alleviates class imbalance in object detection by down-weighting the loss of well-classified examples. Recent research has shown that these two loss functions derived from cross entropy have valuable applications in the field of image segmentation. However, to the best of our knowledge, there is no unified formulation that combines these two loss functions so that t…
University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；Amazon（亚马逊公司）
Pavement conditions are a critical aspect of asset management and directly affect safety. This study introduces a deep neural network method called U-Net for pavement crack segmentation based on drone-captured images to reduce the cost and time needed for airport runway inspection. The proposed approach can also be used for highway pavement conditions assessment during off-peak periods when there are few vehicles on the road. In this study, runway pavement images are collected using drone at various heights from the Fitchburg Municipal Airport (FMA) in Massachusetts to evaluate their quality a…
National Technical University of Athens（雅典国立技术大学）；University of Macedonia（马其顿大学）
Recent studies indicate that deep learning plays a crucial role in the automated visual inspection of road infrastructures. However, current learning schemes are static, implying no dynamic adaptation to users' feedback. To address this drawback, we present a few-shot learning paradigm for the automated segmentation of road cracks, which is based on a U-Net architecture with recurrent residual and attention modules (R2AU-Net). The retraining strategy dynamically fine-tunes the weights of the U-Net as a few new rectified samples are being fed into the classifier. Extensive experiments show that…
