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concrete crack detection using lightweight attention feature fusion single shot multibox detector

KNOWLEDGE-BASED SYSTEMS 2023年第1期261卷

作者： Zhu, Wei Zhang, Hui Eastwood, Joe Qi, Xiaolong Jia, Jiale Cao, Youren Jiangsu Univ Sci & Technol Sch Mech Engn Zhenjiang 212003 Peoples R China Univ Nottingham Mfg Metrol Team Nottingham NG8 1BB England

As one of the most important defects of concrete, cracks seriously threaten the service life and safety of concrete structures, and various safety incidents caused by the collapse of concrete structures have occurred. Therefore, it is essential to detect concrete cracks as soon as possible. Existing object detection methods have low detection accuracy for cracks, leading to unsatisfactory detection results. In this paper, we propose a variety of feasible modules that improve the accuracy of single shot multibox detection (SSD), which is the most efficient object detection method in terms of both accuracy and speed. First, to improve the neural network's ability to learn high-level and low-level feature maps, we propose a feature fusion enhancement module (FFEM). Second, to more effectively capture the information between feature map channels, we propose convolutional network attention (CNA). Third, to improve the anchor box fit to the ground truth box, we reset the distribution of the anchor box. Last, we propose a new type of nonmaximum suppression (NMS) named T-Soft NMS to address numerous issues with current NMS and to significantly enhance the performance of the model. We tested our method on a crack dataset, and numerous tests showed that it outperformed competing methods. In addition, we carried out ablation studies to confirm the validity and efficacy of our method.(c) 2022 Published by Elsevier B.V.

关键词： Deep learning concrete crack detection Feature fusion enhancement module Attention mechanism T-Soft NMS

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Vision-Based concrete crack detection Using a Convolutional Neural Network 1

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35th IMAC Conference and Exposition on Structural Dynamics

作者： Cha, Young-Jin Choi, Wooram Univ Manitoba Dept Civil Engn Winnipeg MB R3T 5V6 Canada Univ Manitoba Dept Civil Engn Winnipeg MB R3T 6B3 Canada

ISBN: (数字)9783319547770

ISBN: (纸本)9783319547770;9783319547763

The prominent methods for monitoring structures to date rely on analyzing data measured from contact sensors that are physically attached to a structure. However, these approaches have the high possibility of false alarms due to noises, sensor malfunctions, and complex environmental effects. Under those circumstances, engineers have to conduct on-site investigations to confirm that damage has occurred. To address this challenge, this paper proposes a new vision-based approach for detecting concrete cracks using a convolutional neural network (CNN). Images are firstly taken under uncontrolled situations to collect widely varying crack features. Second, the raw images are divided into 40K images to build training and validation sets. Lastly, the prepared datasets are fed into a deep CNN architecture with eight layers including convolution, pooling, ReLU, and softmax. The trained classifier consequently records 98% of accuracies in both training and validation.

关键词： concrete crack detection Convolutional neural network Structural health monitoring Vision-based Machine learning

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Multi-Scale Deep Pixel Distribution Learning for concrete crack detection 25

Multi-Scale Deep Pixel Distribution Learning for Concrete Cr...

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25th International Conference on Pattern Recognition (ICPR)

作者： Wu, Xuanyi Ma, Jianfei Sun, Yu Zhao, Chenqiu Basu, Anup Univ Alberta Edmonton AB Canada

ISBN: (纸本)9781728188089

A number of methods including image processing technologies (IPTs) and deep learning methods, have been used to detect defects in civilian infrastructure. These methods have been introduced to extract features representing cracks in concrete surfaces. Inspired by recent advances of a pixel distribution learning method in background subtraction, we propose a novel multi-scale deep learning method (MS-DPDL) for concrete crack detection. The designed CNN network is trained on the dataset crack500 [1], [2] and tested on it for concrete segmentation. To show good transferability of our proposed model, it is later tested on the dataset concrete crack Images for Classification [3]. Several existing deep learning methods are used to compare the performance of the proposed MS-DPDL method. Results show that our method has good performance and can effectively find concrete cracks in practical situations.

关键词： image processing technologies (IPTs) deep learning pixel distribution learning CNN concrete crack detection

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Extended efficient convolutional neural network for concrete crack detection with illustrated merits

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AUTOMATION IN CONSTRUCTION 2023年 156卷

作者： Fu, Ronghua Cao, Maosen Novak, Drahomir Qian, Xiangdong Alkayem, Nizar Faisal Hohai Univ Coll Mech & Mat Nanjing Peoples R China Hohai Univ Inst Struct Dynam & Control Nanjing Peoples R China Jiangxi Univ Sci & Technol Jiangxi Prov Key Lab Environm Geotech Engn & Hazar Ganzhou Peoples R China Brno Univ Technol Inst Struct Mech Fac Civil Engn Brno Czech Republic Hohai Univ Coll Civil & Transportat Engn Nanjing 210098 Peoples R China

An efficient convolutional neural network (CNN), called EfficientNetV2, was recently developed. The early blocks of EfficientNetV2 have structural characteristics that lead to higher training speeds than state-of-the-art CNNs. Inspired by EfficientNetV2, extended research was conducted in this study to determine whether the early, middle, and late blocks of CNNs should have respective structural characteristics to achieve higher efficiency. Based on comprehensive studies, three tactics were proposed, which underpinned a swift CNN called StairNet. StairNet was subsequently equipped into faster region-based CNN framework, producing Faster R-Stair. The presented StairNet and Faster R-Stair were validated on two datasets, respectively: Dataset1 comprising a pair of open-source datasets and a dataset of images captured in real-world conditions;Dataset2 derived from Dataset1, consisting of more complicated object modes, with the purpose of mimicking the coexistence of multiple cracks under real conditions. Experimental results showed that StairNet outperforms EfficientNetV2, GoogLeNet, VGG16_BN, ResNet34, and MobileNetV3 in efficiency of crack classification and detection. A Faster R-Stair concrete crack-detection software platform was also developed. The software platform and an unmanned aerial vehicle were used to detect concrete road cracks at a university in Nanjing, China. The developed system has a swift detection process, with high speed and excellent results.

关键词： EfficientNet Three tactics CNN performance improvement StairNet concrete crack detection Unmanned aerial vehicle Software

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Enhanced concrete crack detection and proactive safety warning based on I-ST-UNet model

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AUTOMATION IN CONSTRUCTION 2024年 166卷

作者： Zhang, He Ma, Leyuan Yuan, Zhenmin Liu, Hexu Qilu Inst Technol Sch Civil Engn Jinan 250200 Shandong Peoples R China Tech Univ Berlin Dept Civil Syst Engn Gustav Meyer Allee 25 D-13355 Berlin Germany Shandong Jianzhu Univ Sch Management Engn Jinan 250101 Shandong Peoples R China Western Michigan Univ Dept Civil & Construct Engn Kalamazoo MI 49009 USA

Existing Swin-Transformer-UNet models for concrete crack detection have several limitations, such as weak feature extraction and loss of detailed image information. This paper thus presents an improved SwinTransformer-UNet (I-ST-UNet) model by embedding Swin-Transformer into UNet. An experimental dataset with 2030 images was used to train and test the I-ST-UNet model. The model showed significant segmentation performance improvements: 0.7% in Accuracy, 2.25% in Mean Accuracy, 5.77% in Mean Intersection-OverUnion, and 1% in Frequency Weight Intersection-Over-Union. Relative errors for crack widths from 0.1 mm to 0.2 mm and those exceeding 0.2 mm consistently remained below 5%. For crack widths over 0.2 mm, the model achieved a remarkable 98.35% accuracy in safety warning. Important features of the I-ST-UNet model include (i) adding External Attention to the skip connection layer to enhance the potential relationship between samples;(ii) improving Feature Pyramid Network to condense features at different scales during upsampling in the decoding phase.

关键词： Computer vision Swin-transformer-UNet concrete crack detection crack width calculation Safety warning

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Towards a Remote Sensing System for Railroad Bridge Inspections: A concrete crack detection Component

Towards a Remote Sensing System for Railroad Bridge Inspecti...

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IEEE SoutheastCon

作者： Luis Daniel Otero Mark Moyou Adrian Peter Carlos E. Otero Department of Engineering Systems Florida Institute of Technology Melbourne FL USA Department of Electrical and Computer Engineering Florida Institute of Technology Melbourne FL USA

ISBN: (纸本)9781538661345

This research paper presents ongoing research work towards developing an effective remote sensing system for bridge inspections. The paper describes the development and evaluation of a five-step prototype image processing algorithm to detect concrete cracks. The algorithm, based on an unsupervised learning approach, extracts pixels that belong to concrete cracks from images. The algorithm was tested using various images collected from field tests. The images included different types of concrete cracks, along with various types of noise factors. The algorithm was successful in detecting the different types of concrete cracks from various images. Contrary to most studies from the literature, these images were not "clean". That is, the images were collected from field tests and used as inputs to the detection algorithm without any preprocessing activity.

关键词： Structural inspections Bridge inspections concrete crack detection Transportation systems engineering

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An Automatic concrete crack-detection Method Fusing Point Clouds and Images Based on Improved Otsu's Algorithm

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SENSORS 2021年第5期21卷 1581-1581页

作者： Chen, Xiaolong Li, Jian Huang, Shuowen Cui, Hao Liu, Peirong Sun, Quan Zhengzhou Univ Sch Water Conservancy Sci & Engn Zhengzhou 450001 Peoples R China Zhengzhou Univ Sch Geosci & Technol Zhengzhou 450001 Peoples R China

cracks are one of the main distresses that occur on concrete surfaces. Traditional methods for detecting cracks based on two-dimensional (2D) images can be hampered by stains, shadows, and other artifacts, while various three-dimensional (3D) crack-detection techniques, using point clouds, are less affected in this regard but are limited by the measurement accuracy of the 3D laser scanner. In this study, we propose an automatic crack-detection method that fuses 3D point clouds and 2D images based on an improved Otsu algorithm, which consists of the following four major procedures. First, a high-precision registration of a depth image projected from 3D point clouds and 2D images is performed. Second, pixel-level image fusion is performed, which fuses the depth and gray information. Third, a rough crack image is obtained from the fusion image using the improved Otsu method. Finally, the connected domain labeling and morphological methods are used to finely extract the cracks. Experimentally, the proposed method was tested at multiple scales and with various types of concrete crack. The results demonstrate that the proposed method can achieve an average precision of 89.0%, recall of 84.8%, and F1 score of 86.7%, performing significantly better than the single image (average F1 score of 67.6%) and single point cloud (average F1 score of 76.0%) methods. Accordingly, the proposed method has high detection accuracy and universality, indicating its wide potential application as an automatic method for concrete-crack detection.

关键词： concrete crack detection the fusion of point clouds and images 3D laser point cloud Otsu’ s algorithm

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CCDFormer: A dual-backbone complex crack detection network with transformer

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PATTERN RECOGNITION 2025年 161卷

作者： Hu, Xiangkun Li, Hua Feng, Yixiong Qian, Songrong Li, Jian Li, Shaobo Guizhou Univ State Key Lab Publ Big Data Guiyang 550025 Guizhou Peoples R China Tsinghua Univ Dept Mech Engn Beijing 100084 Peoples R China Zhejiang Univ Sch Mech Engn Hangzhou 310058 Zhejiang Peoples R China

concrete crack detection is a critical aspect of infrastructure maintenance. However, existing methods often fail to deliver satisfactory results in real-world scenarios where various detection challenges coexist. We propose a Transformer-based model to enhance feature extraction for complex crack detection (CCDFormer). CCDFormer employs a dual-backbone U-shaped structure to independently capture crack features from different perspectives, avoiding interference. Deformable linear convolution align with crack structures, while the proposed feature enhancement module enriches semantic features by boosting local features at multiple scales. The pyramid-shaped Transformer models long-range dependencies across different scales. A carefully designed feature fusion module addresses the shortcomings of local and contextual features, generating robust crack features. On a challenging public dataset for concrete crack detection, CCDFormer improves accuracy, recall, Fmeasure, and IoU by 3.54%, 0.71%, 2.17%, and 1.48%, compared to existing models. CCDFormer demonstrates higher precision and crack detection rates across various challenges, proving practical for real-world crack detection.

关键词： concrete crack detection Semantic segmentation Transformer Convolution neural network UNet

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Lightweight multi-scale encoder- decoder network with locally enhanced attention mechanism for concrete crack segmentation

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第2期36卷 025021-025021页

作者： Dong, Shuai Cao, Jin Wang, Yunlong Ma, Jia Kuang, Zhiwei Zhang, Zhenhao Changsha Univ Sci & Technol Sch Civil Engn Changsha 410114 Peoples R China Saarland Univ Inst Appl Mech D-66123 Saarbrucken Germany

concrete surface crack detection and maintenance are crucial for ensuring structural safety. Deep learning-based techniques for detecting concrete cracks have become popular due to the quick advancement of artificial intelligence. However, the actual uses of these methods are limited due to issues like large model sizes and significant dependence on powerful computing hardware. To address these issues, this paper presents a lightweight multi-scale encoder-decoder network (LMED-Net) for crack detection of concrete structures. LMED-Net employs MobileNetV2 as the encoder for the initial feature extraction. A multi-scale feature extraction (MFE) module is developed and serially attached after the encoder for refining feature extraction. Finally, to strengthen the network's perception of pixels surrounding the cracks, a novel enhanced attention mechanism (EAM) is deployed in the decoder. By improving the network's attention to information within the crack regions, this mechanism keeps contextual information from being lost. Comparative experimental results show that the proposed network achieves an F1 score (F1) of 60.32% and a mean intersection over union (mIoU) of 71.04% on the crack forest dataset. On the Deepcrack dataset, the F1 and mIoU increase to 79.09% and 81.85% respectively. Notably, LMED-Net performs exceptionally well in crack segmentation since its model size and parameters count are much smaller than those of other image segmentation methods. Furthermore, ablation studies further validate the effectiveness of the proposed MFE module and EAM.

关键词： concrete crack detection semantic segmentation lightweight network encoder-decoder network local enhanced attention mechanism

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Automated vision-based concrete crack measurement system

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MEASUREMENT 2025年第PartA期242卷

作者： Giulietti, Nicola Revel, Gian Marco Chiariotti, Paolo Univ Pavia Dipartimento Ingn Ind & Informaz Via Adolfo Ferrata 5 I-27100 Pavia Italy Univ Politecn Marche Dept Ind Engn & Math Sci Via Brecce Bianche 12 I-60131 Ancona Italy Politecn Milan Dept Mech Engn Via Masa 1 I-20156 Milan Italy

Maintaining and ensuring the safety of ageing infrastructures is becoming of uttermost importance. This is particularly critical in Europe, where several infrastructures like tunnels and bridges are approaching their end-of-life. Such a condition justifies the need for a prompt recognition of the deterioration status of these structures as well as the identification of strategies for effective inspection and monitoring operations. This paper focuses on cracks, and proposes a vision-based measurement system aimed at precisely and accurately detecting cracks on concrete surfaces and measuring their geometric features. The system mainly leverages RGB-D cameras to make the pixel to real world measurement units conversion possible (by assessing the camera to target relative distance and pose), and exploits an image processing pipeline consisting of the following steps: (a) an artificial intelligence-based semantic segmentation model for identifying cracks on the target structure;(b) a ridge detection algorithm for identifying the centre line of the cracks;(c) a geometric feature extraction algorithm to extract key metrics of the crack, like its width and length. Variance analysis of the crack mean width measurement shows a repeatability standard deviation of 0.01 mm and a reproducibility standard deviation of 0.02 mm, with a Type A expanded uncertainty of 0.004 mm. The system performance is discussed in the paper referring both to lab testing activities, targeted to the assessment of its metrological characteristics, and to a real field application, i.e. concrete inspection in a tunnel. This latter application is of particular interests as it is targeted to demonstrate the robustness of the approach in harsh environments (i.e., strong presence of dust, total absence of natural light) and on surfaces whose roughness generates important light gradients and shadows (e.g., sprayed concrete).

关键词： Vision-based measurement system concrete crack measurement concrete crack detection Surface crack geometric feature extraction Uncertainty analysis

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