Surface Flaw Classification Based on Dual Cross Pattern

Mentouri, Zoheir; Doghmane, Hakim; Moussaoui, Abdelkrim; Boudjehem, Djalil

doi:10.1109/ccssp49278.2020.9151504

Cited by 8 publications

(3 citation statements)

References 5 publications

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“…There are three categories of deep learning-based defect inspection systems, which are based on classification, object detection, and object segmentation. Classification-based defect inspection systems [13][14][15][16][17][18][19][20][21] categorize an input image as defect or non-defect and calculate the class probability using stacks of convolutional neural networks. This approach is simple, but it cannot localize defects.…”

Section: Introductionmentioning

confidence: 99%

FDD: a deep learning–based steel defect detectors

Akhyar

Liu

Hsu

et al. 2023

Int J Adv Manuf Technol

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Surface defects are a common issue that affects product quality in the industrial manufacturing process. Many companies put a lot of effort into developing automated inspection systems to handle this issue. In this work, we propose a novel deep learning–based surface defect inspection system called the forceful steel defect detector (FDD), especially for steel surface defect detection. Our model adopts the state-of-the-art cascade R-CNN as the baseline architecture and improves it with the deformable convolution and the deformable RoI pooling to adapt to the geometric shape of defects. Besides, our model adopts the guided anchoring region proposal to generate bounding boxes with higher accuracies. Moreover, to enrich the point of view of input images, we propose the random scaling and the ultimate scaling techniques in the training and inference process, respectively. The experimental studies on the Severstal steel dataset, NEU steel dataset, and DAGM dataset demonstrate that our proposed model effectively improved the detection accuracy in terms of the average recall (AR) and the mean average precision (mAP) compared to state-of-the-art defect detection methods. We expect our innovation to accelerate the automation of industrial manufacturing process by increasing the productivity and by sustaining high product qualities.

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Section: Introductionmentioning

confidence: 99%

FDD: a deep learning–based steel defect detectors

Akhyar

Liu

Hsu

et al. 2023

Int J Adv Manuf Technol

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“…They have high requirements for image data, and they are unable to meet production needs due to their low efficiency and poor adaptability. [ 5 ] With the emergence and development of convolutional neural network (CNN), convolution kernels with stronger feature extraction capability and greater versatility have successfully replaced manual feature extraction. Consequently, target detection technology has begun to develop rapidly.…”

Section: Introductionmentioning

confidence: 99%

An Improved YOLOX Model for Detecting Strip Surface Defects

Chen

et al. 2022

steel research int.

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During the process of producing hot‐rolled strips in the metallurgical industry, various defects inevitably appear on its surface due to harsh environments and complex manufacturing, consequently bringing about quality problems and economic loss. However, the existing detection methods are difficult to meet the actual requirements of commercial production due to their problems, such as low efficiency and low accuracy. Herein, an improved You only look once X (YOLOX) model for detecting strip surface defects is proposed. Based on the existing YOLOX model, herein, the MobileViT block is introduced to enhance the capability of feature extraction of the backbone network output. The feature pyramid networks through efficient channel attention (ECA) module to strengthen important channel weights are improved, and finally, the original positioning loss function by efficient intersection over union (EIOU) to increase the locating accuracy is replaced. The experimental results show that the improved YOLOX model can obtain 80.67 mAP and 75.69 mAP detection effects on the Northeast University dataset and Xsteel surface defect dataset, respectively. Compared with the original YOLOX, the model increases by 3.95 mAP and 4.02 mAP, respectively. The data fully show that the improved YOLOX model proposed herein is more effective for strip surface defect detection.

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“…Due to their high representational ability, local texture descriptors-based approaches have been used extensively in many applications of computer vision. Among them: Local Phase Quantization (LPQ) [13], Local Binary Pattern (LBP) [14], Binarized Statistical Image Feature (BSIF) [15] and Dual Cross Pattern (DCP) [16]. The reason for employing local texture descriptors for palmprint recognition is the possibility of treating the palmprint image as micro-patterns compositions that are properly characterized by such descriptors.…”

Section: Introductionmentioning

confidence: 99%

Touchless Palmprint Identification Based on Patch Cross Pattern Representation

Doghmane

Messaoudi

Korba

et al. 2021

Lecture Notes in Electrical Engineering

Self Cite

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Over the last decade, palmprint recognition has been studied for many problems and applications. It has become one of the most well-known biometric recognition system. Its success is due to the rich features that can be extracted and exploited from the palmprint images captured by contact or contactless device. This paper presents a new representation based on textural structure of human palms for touchless palmprint identification. This representation method is called Patch Cross Pattern (PCP), which relies mainly on cross pattern encoder and the nonoverlapping decomposition method. The feature vector is built using Cross Pattern (CP) encoder to capture the textural structure of palmprint image. Then, the nonoverlapping decomposition on both directions is applied. Next, the feature vector representation of each palmprint image is constructed by concatenating all normalized histograms calculated at each patch. In addition, the reduced version of the PCP called R-PCP is obtained using whitened linear discriminant analysis. Finally, a Knearest neighbor classifier is used for palmprint identification. The proposed system is successfully applied to IIT Delhi and CASIA touchless databases. Results show that, the proposed representation provides a significant performance improvement compared to the recent state-of-the-art in terms of accuracy.

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Surface Flaw Classification Based on Dual Cross Pattern

Cited by 8 publications

References 5 publications

FDD: a deep learning–based steel defect detectors

FDD: a deep learning–based steel defect detectors

An Improved YOLOX Model for Detecting Strip Surface Defects

Touchless Palmprint Identification Based on Patch Cross Pattern Representation

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