Classification of Magnetic Tile Surface Defects Based on Efficientnet Network with Attention

Abstract The magnetic tile image has the characteristics of uneven illumination, complex surface texture, and low contrast. Aiming at the problem that the traditional defect detection algorithm is difficult to accurately identify the defects, and the deep learning algorithm is difficult to balance the classification accuracy and the size of the speed model, a defect classification algorithm based on attention-based EfficientNet is proposed. The algorithm first enhances the network’s spatial and location information for image features by integrating the Convolutional Block Attention Module, and improves the network’s ability to identify defects. Then, on this basis, Criss-Cross Attention is added to the network, so that the network can better the context information of the horizontal and vertical cross of image features, so that each pixel can finally capture the full image dependency of all pixels. Experimental results show that the algorithm has higher classification accuracy than EfficientNet-B0, reached 99.11%, and has a better balance between accuracy, speed and model size than other classification models.

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Classification of Shellfish Recognition Based on Improved Faster R-CNN Framework of Deep Learning

Mathematical Problems in Engineering ◽

10.1155/2021/1966848 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Yiran Feng ◽

Xueheng Tao ◽

Eung-Joo Lee

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Experimental Tests ◽

Detection Algorithm ◽

Detection Accuracy ◽

Multiple Objects ◽

Detection Model ◽

Deep Learning Algorithm ◽

Quality Sorting

In view of the current absence of any deep learning algorithm for shellfish identification in real contexts, an improved Faster R-CNN-based detection algorithm is proposed in this paper. It achieves multiobject recognition and localization through a second-order detection network and replaces the original feature extraction module with DenseNet, which can fuse multilevel feature information, increase network depth, and avoid the disappearance of network gradients. Meanwhile, the proposal merging strategy is improved with Soft-NMS, where an attenuation function is designed to replace the conventional NMS algorithm, thereby avoiding missed detection of adjacent or overlapping objects and enhancing the network detection accuracy under multiple objects. By constructing a real contexts shellfish dataset and conducting experimental tests on a vision recognition seafood sorting robot production line, we were able to detect the features of shellfish in different scenarios, and the detection accuracy was improved by nearly 4% compared to the original detection model, achieving a better detection accuracy. This provides favorable technical support for future quality sorting of seafood using the improved Faster R-CNN-based approach.

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Computer vision for automatic detection and classification of fabric defect employing deep learning algorithm

International Journal of Clothing Science and Technology ◽

10.1108/ijcst-11-2018-0135 ◽

2019 ◽

Vol 31 (4) ◽

pp. 510-521 ◽

Cited By ~ 12

Author(s):

Pandia Rajan Jeyaraj ◽

Edward Rajan Samuel Nadar

Keyword(s):

Defect Detection ◽

Classification Accuracy ◽

Learning Algorithm ◽

Data Sets ◽

Defect Classification ◽

Content Type ◽

Deep Learning Algorithm ◽

Average Accuracy ◽

Fabric Defect

Purpose The purpose of this paper is to focus on the design and development of computer-aided fabric defect detection and classification employing advanced learning algorithm. Design/methodology/approach To make a fast and effective classification of fabric defect, the authors have considered a characteristic of texture, namely its colour. A deep convolutional neural network is formed to learn from the training phase of various defect data sets. In the testing phase, the authors have utilised a learning feature for defect classification. Findings The improvement in the defect classification accuracy has been achieved by employing deep learning algorithm. The authors have tested the defect classification accuracy on six different fabric materials and have obtained an average accuracy of 96.55 per cent with 96.4 per cent sensitivity and 0.94 success rate. Practical implications The authors had evaluated the method by using 20 different data sets collected from different raw fabrics. Also, the authors have tested the algorithm in standard data set provided by Ministry of Textile. In the testing task, the authors have obtained an average accuracy of 94.85 per cent, with six defects being successfully recognised by the proposed algorithm. Originality/value The quantitative value of performance index shows the effectiveness of developed classification algorithm. Moreover, the computational time for different fabric processing was presented to verify the computational range of proposed algorithm with the conventional fabric processing techniques. Hence, this proposed computer vision-based fabric defects detection system is used for an accurate defect detection and computer-aided analysis system.

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Review for "Smart grid security enhancement by detection and classification of non‐technical losses employing deep learning algorithm"

10.1002/2050-7038.12521/v1/review4 ◽

2020 ◽

Keyword(s):

Deep Learning ◽

Smart Grid ◽

Learning Algorithm ◽

Grid Security ◽

Security Enhancement ◽

Smart Grid Security ◽

Deep Learning Algorithm

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Review for "Smart grid security enhancement by detection and classification of non‐technical losses employing deep learning algorithm"

10.1002/2050-7038.12521/v1/review2 ◽

2020 ◽

Keyword(s):

Deep Learning ◽

Smart Grid ◽

Learning Algorithm ◽

Grid Security ◽

Security Enhancement ◽

Smart Grid Security ◽

Deep Learning Algorithm

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Faculty Opinions recommendation of Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.732648119.793554280 ◽

2018 ◽

Author(s):

Christine Ko

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Clinical Images ◽

Deep Learning Algorithm

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Automated Detection and Classification of Desmoplastic Reaction at the Colorectal Tumour Front Using Deep Learning

Cancers ◽

10.3390/cancers13071615 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1615

Author(s):

Ines P. Nearchou ◽

Hideki Ueno ◽

Yoshiki Kajiwara ◽

Kate Lillard ◽

Satsuki Mochizuki ◽

...

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Prognostic Significance ◽

Stage Ii ◽

Full Cohort ◽

Test Set ◽

Colorectal Tumour ◽

Desmoplastic Reaction ◽

Deep Learning Algorithm

The categorisation of desmoplastic reaction (DR) present at the colorectal cancer (CRC) invasive front into mature, intermediate or immature type has been previously shown to have high prognostic significance. However, the lack of an objective and reproducible assessment methodology for the assessment of DR has been a major hurdle to its clinical translation. In this study, a deep learning algorithm was trained to automatically classify immature DR on haematoxylin and eosin digitised slides of stage II and III CRC cases (n = 41). When assessing the classifier’s performance on a test set of patient samples (n = 40), a Dice score of 0.87 for the segmentation of myxoid stroma was reported. The classifier was then applied to the full cohort of 528 stage II and III CRC cases, which was then divided into a training (n = 396) and a test set (n = 132). Automatically classed DR was shown to have superior prognostic significance over the manually classed DR in both the training and test cohorts. The findings demonstrated that deep learning algorithms could be applied to assist pathologists in the detection and classification of DR in CRC in an objective, standardised and reproducible manner.

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