Synergizing Low Rank Representation and Deep Learning for Automatic Pavement Crack Detection

Gao, Zhi; Zhao, Xuhui; Cao, Min; Li, Ziyao; Liu, Kangcheng; Chen, Ben M.

doi:10.1109/tits.2023.3275570

Cited by 7 publications

(3 citation statements)

References 70 publications

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“…[13] and Gao et al. [14] developed a new pyramid structure to make greater use of multi‐scale features while avoiding background interference from pavement crack photos as much as possible. Jiao et al.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, deep learning-based methods have become increasingly popular in the fields of defect recognition [13][14][15][16][17], fault diagnosis [8], industrial process soft sensing [9][10][11], and so on. Deep learning algorithms are used for defect identification in a variety of tasks, such as classification, location, detection, segmentation, and others.…”

Section: Introductionmentioning

confidence: 99%

“…Deep learning's success is progressively replacing classic pattern recognition tasks with image detection tasks [12]. Lu et al [13] and Gao et al [14] developed a new pyramid structure to make greater use of multi-scale features while avoiding background interference from pavement crack photos as much as possible. Jiao et al [15] used multi-scale characteristics and context information to remove interference from complicated backgrounds and related objects in bolt defect images.…”

Section: Introductionmentioning

confidence: 99%

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MACN: A cascade defect detection for complex background based on mixture attention mechanism

Zhao,

Wu,

Yuan

et al. 2024

IET Image Processing

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Defect detection in complex background is a critical issue. To address this issue, this paper proposes the mixture attention mechanism cascade network, in which the new channel attention network is linked with the spatial attention network to create an effective mixed attention network that takes advantage of their respective advantages, adaptively suppresses background noise, and highlights defect features. To ensure the efficiency and effectiveness of effective mixed attention network, the new channel attention network splices the output features of the global average pooling layer and the global maximum pooling layer and then sends the spliced features into a shared network, which is a one‐dimensional convolutional network, and uses cross‐channel interaction for fusion. Furthermore, in order to provide more discriminative feature representation, the authors extract the intermediate features of the region proposal network and input them into effective mixed attention network. Finally, the cascade head is used to refine the predicted bounding box to achieve high‐quality defect location. To demonstrate the superiority and usefulness of this method, it is compared to the latest method using widely used PCB and NEU data sets. A large number of trials demonstrate that this strategy outperforms other methods for detecting defects in complicated backgrounds.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MACN: A cascade defect detection for complex background based on mixture attention mechanism

Zhao,

Wu,

Yuan

et al. 2024

IET Image Processing

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A Novel MAE-Based Self-Supervised Anomaly Detection and Localization Method

Chen,

Peng,

Huang

et al. 2023

IEEE Access

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Despite significant advancements in self-supervised anomaly detection, multi-class anomaly detection tasks still pose substantial challenges. Most existing methods require individual network training for each category of objects. This paper presents a novel end-to-end approach for multi-class anomaly detection: selfsupervised Mask-pretrained Anomaly Localization Autoencoder (MALA). Firstly, the masked autoencoder (MAE) and Pseudo Label Prediction Module (PLPM) are utilized to recover and perceive normal image patterns. Subsequently, the encoder weights are frozen for further end-to-end network training to predict anomalous maps directly. Token Balance Module(TBM) facilitates anomalous perception and improves anomaly segmentation. By utilizing the Visual Transformer and employing image inpainting as a proxy task, remarkable generalization results are achieved. The proposed method demonstrates its applicability across diverse styles of industrial products. Experiments are conducted on MVTech AD, VisA, KolektorSDD2, and MT datasets, achieving state-of-the-art results in multi-task anomaly detection and segmentation tasks. Specifically, we obtain image AUROC of 98.% and pixel AUROC of 97.1% on MVTech AD dataset, pixel AUROC of 97.1% on VisA dataset, and pixel AUROC of 98.7% on KolektorSDD2 dataset.

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Enrich, Distill and Fuse: Generalized Few-Shot Semantic Segmentation in Remote Sensing Leveraging Foundation Model’s Assistance

Gao,

Ao,

Wang

et al. 2024

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Synergizing Low Rank Representation and Deep Learning for Automatic Pavement Crack Detection

Cited by 7 publications

References 70 publications

MACN: A cascade defect detection for complex background based on mixture attention mechanism

MACN: A cascade defect detection for complex background based on mixture attention mechanism

A Novel MAE-Based Self-Supervised Anomaly Detection and Localization Method

Enrich, Distill and Fuse: Generalized Few-Shot Semantic Segmentation in Remote Sensing Leveraging Foundation Model’s Assistance

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