Automatic Crack Detection using Mask R-CNN

Attard, Leanne; Debono, Carl James; Valentino, Gianluca; Castro, Mario Di; Masi, A.

doi:10.1109/ispa.2019.8868619

Cited by 63 publications

(29 citation statements)

References 22 publications

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“…For this purpose, authors labelled 2,366 images with the size of 500×375 for training. Attard et al (2019) trained a Mask R-CNN with 200 images to locate cracks on the concrete surface at pixel level. Kim and Cho (2019) used 376 images in their training data for Mask R-CNN to find the cracks on a concrete wall with high resolution cameras and utilized an additional image processing procedure on each bounding box to quantitatively measure the width of these cracks.…”

Section: Spalling and Cracks Detection With Deep Learningmentioning

confidence: 99%

Detecting Cracks and Spalling Automatically in Extreme Events by End-to-End Deep Learning Frameworks

Bai

Sezen

Yilmaz

2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In this paper, we develop and implement end-to-end deep learning approaches to automatically detect two important types of structural failures, cracks and spalling, of buildings and bridges in extreme events such as major earthquakes. A total of 2,229 images were annotated, and are used to train and validate three newly developed Mask Regional Convolutional Neural Networks (Mask R-CNNs). In addition, three sets of public images for different disasters were used to test the accuracy of these models. For detecting and marking these two types of structural failures, one of proposed methods can achieve an accuracy of 67.6% and 81.1%, respectively, on low- and high-resolution images collected from field investigations. The results demonstrate that it is feasible to use the proposed end-to-end method for automatically locating and segmenting the damage using 2D images which can help human experts in cases of disasters.

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Section: Spalling and Cracks Detection With Deep Learningmentioning

confidence: 99%

Detecting Cracks and Spalling Automatically in Extreme Events by End-to-End Deep Learning Frameworks

Bai

Sezen

Yilmaz

2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Second, a comparative analysis of different baseline network models (ZF-Net, VGG-16, ResNet-50, and ResNet-101) and different target detection algorithms (SSD Algorithms, YOLO V2 Algorithms, Faster R-CNN Algorithms, and ME-Faster R-CNN Algorithms) was performed, showing the best performance for ResNet networks and ME-Faster R-CNN, respectively. The research by Leanne Attard et al [26] demonstrated an automated crack detection model using Mask R-CNN, which was trained on its own ground truth database, which was a subset of the regular crack dataset. After training the model, the precision value achieved was 93.94% and the recall value was 77.5%.…”

Section: B R-cnn Deep Learning Model Familymentioning

confidence: 99%

Automatic Multiclass Instance Segmentation of Concrete Damage Using Deep Learning Model

2021

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Concrete is one of the primary and most commonly used materials for the construction of buildings, roads, bridges, and dams. But it may lose its strength with age, moisture content, or due to other factors. As a result, little damage (crack and spall) to these structures may lead to sudden collapse or breakage, which in turn can decimate many lives with economic losses. Hence, to ensure their strength with the proper load, accurate screening of concrete surface damage is necessary for maintenance engineers to understand and evaluate the severity of the damage. Few preceding vision-based studies have proposed object detection and semantic segmentation approaches to carry out damage detection; however, the developed models could not segment different objects of the same class. The semantic segmentation approach separates regions that contain only objects of the same class. Concrete surface damage, on the other hand, may contain multiple objects, such as the Horizontal Crack, Vertical Crack, Diagonal Crack, Branch Crack, and Spall, that needs to be segmented separately so that specific measures can be taken. This study utilizes Mask Region-Based Convolutional Neural Network (Mask R-CNN) to manage concrete damage images and employ it to detect and segment defects in civil infrastructure with multiple objects. As we understand, this is the first research involving a Structural Health Monitoring (SHM) application for damage detection using instance segmentation. Our proposed model has been trained and tested on a dataset containing 800 images (480*480 pixels) of different types of crack and spall are collected from distinct structures of CSIR-CEERI, Pilani, campus. To test and validate the generosity, an additional 96 damage images are downloaded using Google. The pre-processed and annotated images are used to train and validate the Mask R-CNN Classifier. Our experimental results show that damage can be classified efficiently with 95.13% accuracy on a custom dataset and 96.87% on randomly picked images.

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“…We verified that the proposed bi-CRRN could successfully detect and localize the welding defects. The performance of the proposed bi-CRRN was compared with recent defect detection algorithms such as mask-RCNN [30], U-Net [31], DeepLabv3 [32], 3D-CNN [33], ConvLSTM, and CRRN. The network architectures of 3D-CNN and ConvLSTM, which exploit spatial and temporal information, were implemented based on the CRRN architecture.…”

Section: Pixel-level Performance Evaluationmentioning

confidence: 99%

Bi-Directional Convolutional Recurrent Reconstructive Network for Welding Defect Detection

et al. 2021

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Nowadays, the welding process is essential in various manufacturing industrial fields, such as aerospace, vehicle production, and shipbuilding. The welding defects caused in the process need to be monitored as they can cause serious accidents and losses. Traditional computer vision methods in an industrial application are inefficient when the detection targets have variations in shape, scale, and color because the detection performance depends on the hand-crafted features. To overcome this limitation, deep learning models, such as the convolutional neural network (CNN), are applied to industrial defect detection. These CNN-based models trained on static images, however, a low performance that cannot meet the industrial requirements. To deal with the challenge, bidirectional Convolutional Recurrent Reconstructive Network (bi-CRRN) is proposed for welding defect detection and localization based on welding video. Spatio-temporal data, especially the forward and backward sequences, are considered in our bi-CRRN to get high detection performance. Moreover, an automatic defect detection equipment is developed to weld a material and monitor the welding bead simultaneously. We demonstrate that the proposed bi-CRRN outperforms the other segmentation network models in welding defect detection.

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Automatic Crack Detection using Mask R-CNN

Cited by 63 publications

References 22 publications

Detecting Cracks and Spalling Automatically in Extreme Events by End-to-End Deep Learning Frameworks

Detecting Cracks and Spalling Automatically in Extreme Events by End-to-End Deep Learning Frameworks

Automatic Multiclass Instance Segmentation of Concrete Damage Using Deep Learning Model

Bi-Directional Convolutional Recurrent Reconstructive Network for Welding Defect Detection

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