Automatic defect detection and segmentation of tunnel surface using modified Mask R-CNN

Xu, Yingying; Li, Dawei; Xie, Qian; Wu, Qiaoyun; Wang, Jun

doi:10.1016/j.measurement.2021.109316

Cited by 138 publications

(46 citation statements)

References 30 publications

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“…Xu et al [34] propose a novel tunnel defect inspection method based on the Mask R-CNN. Xiao and Buffiere [35] developed an image segmentation method based on a convolutional neural network for fatigue crack images.…”

Section: B Deep-learning Techniquesmentioning

confidence: 99%

Deep Learning Based Classification of Military Cartridge Cases and Defect Segmentation

et al. 2022

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The final stage of the production process in the industry is quality control. Quality control answers the question of is there a defect on the surface of the products. Frequently the quality control is performed manually. The disadvantages of manual quality control are high error rate (low accuracy), low product rate (low performance) and high expense rate (high cost). The solution is automatic quality control using machine vision systems. These systems classify the products and segment the defects on their surfaces by processing the images taken by cameras during the production process in real-time. Some products like military cartridge cases have metallic, cylindrical, non-uniform texture and highly reflective surface. So, the quality of images is very important. Another factor that affects the accuracy is the nonuniform texture of the product surface. Distinguishing the product non-uniform texture from defect texture is a challenging problem. In previous works, this problem have been tried to be solved with image processing and deep learning techniques and the accuracy of 97% and 96% have been obtained, appropriately. According to NATO standards, the accuracy of the classification of the military cartridge cases should be above 99%. In this work, the methodology for classification of the military cartridge cases and segmentation of the defects on their surfaces with non-uniform texture is proposed to increase the accuracy. In scope of the proposed methodology the datasets with non-defective, defective and labeled/masked image classes of the cartridge cases were created, the deep learning models to classify the military cartridge cases and segment the defects on their surfaces were proposed, implemented and obtained results were evaluated using the metrics such as Accuracy, Precision, Recall, F1-Score, Jaccard Index (JI) and Mean Intersection over Union (mIoU). Obtained results showed that the proposed methodology increased the accuracy of classification to 100% with the DenseNet169 model and the F1-Score of segmentation to 92.1% with Improved U-Net and ResUnet models.

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Section: B Deep-learning Techniquesmentioning

confidence: 99%

Deep Learning Based Classification of Military Cartridge Cases and Defect Segmentation

et al. 2022

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“…Those augmentation operations are randomly applied to images during training and can contain operations that change the brightness, contrast, or color of an image or include flips and rotations. Those flips along the horizontal and vertical axis, as well as rotations, are popular for cracks [33], [34], [36], [122], as they are usually rotation invariant. This does not apply to several other domains; for example, flipping images of numbers upside down may result in incorrect representations.…”

Section: A Popular Open Datasetsmentioning

confidence: 99%

What's Cracking? A Review and Analysis of Deep Learning Methods for Structural Crack Segmentation, Detection and Quantification

König¹,

Jenkins²,

Mannion³

et al. 2022

Preprint

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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. It outlines the various tasks that are solved through applying computer vision algorithms to surface cracks in a structural health monitoring setting and also provides in-depth reviews of recent fully, semi and unsupervised approaches that perform crack classification, detection, segmentation and quantification. Additionally, this review also highlights popular datasets used for cracks and the metrics that are used to evaluate the performance of those algorithms. Finally, potential research gaps are outlined and further research directions are provided.

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“…Huang et al proposed a Faster R-CNN-based part surface defect detection algorithm based on the cluster generation anchor scheme, and introduced a multi-level ROI pooling layer structure to achieve the efficient and accurate detection of part surface defects [17]. Xu et al proposed a Path-Enhanced Feature Pyramid Network (PEFPN) and an edge detection branch integrated into the Mask R-CNN, and this was applied in tunnel defect detection and segmentation [18]. Zhang et al combined Multi-Scale Overlapping Sliding Pooling (SOSP) and proposed an SSD-based jelly impurity detection method [19].…”

Section: Introductionmentioning

confidence: 99%

An Appearance Defect Detection Method for Cigarettes Based on C-CenterNet

et al. 2022

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Due to the poor adaptability of traditional methods in the cigarette detection task on the automatic cigarette production line, it is difficult to accurately identify whether a cigarette has defects and the types of defects; thus, a cigarette appearance defect detection method based on C-CenterNet is proposed. This detector uses keypoint estimation to locate center points and regresses all other defect properties. Firstly, Resnet50 is used as the backbone feature extraction network, and the convolutional block attention mechanism (CBAM) is introduced to enhance the network’s ability to extract effective features and reduce the interference of non-target information. At the same time, the feature pyramid network is used to enhance the feature extraction of each layer. Then, deformable convolution is used to replace part of the common convolution to enhance the learning ability of different shape defects. Finally, the activation function ACON (ActivateOrNot) is used instead of the ReLU activation function, and the activation operation of some neurons is adaptively selected to improve the detection accuracy of the network. The experimental results are mainly acquired via the mean Average Precision (mAP). The experimental results show that the mAP of the C-CenterNet model applied in the cigarette appearance defect detection task is 95.01%. Compared with the original CenterNet model, the model’s success rate is increased by 6.14%, so it can meet the requirements of precision and adaptability in cigarette detection tasks on the automatic cigarette production line.

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Automatic defect detection and segmentation of tunnel surface using modified Mask R-CNN

Cited by 138 publications

References 30 publications

Deep Learning Based Classification of Military Cartridge Cases and Defect Segmentation

Deep Learning Based Classification of Military Cartridge Cases and Defect Segmentation

What's Cracking? A Review and Analysis of Deep Learning Methods for Structural Crack Segmentation, Detection and Quantification

An Appearance Defect Detection Method for Cigarettes Based on C-CenterNet

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