Surface Defect Detection Using Image Pyramid

Ling, Xiao; Wu, Bo; Hu, Youmin

doi:10.1109/jsen.2020.2977366

Cited by 66 publications

(16 citation statements)

References 40 publications

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“…The MTCNN mode mainly uses three cascaded network systems, such as Proposal Network (P-Net), Refine Network (R-Net), and Output Network (O-Net) to achieve faster and more effective face data monitoring [ 16 ]. This mode also uses key techniques such as image pyramid, border recycling, and off-peak control [ 17 ]. P-Net first changes all training samples into the pixel size of 12 × 12 × 3 images and obtains a 1 × 1 × 32 feature map (obtained after convolution) through three convolutional layers.…”

Section: Design and Research Of Classroom Detection System Based On DLmentioning

confidence: 99%

Analysis of the Current Situation of Teaching and Learning of Ideological and Political Theory Courses by Deep Learning

Chen

Zhang

2022

Computational Intelligence and Neuroscience

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The objectives are to solve the problems existing in the current ideological and political theory courses, such as the difficulty of classroom teaching quality assessment, the confusion of teachers’ classroom process management, and the lack of objective assessment basis in teaching quality monitoring. Based on Artificial Intelligence (AI) technology, a designed evaluation method is proposed for teachers’ classroom teaching and solves some problems such as high system cost, low evaluation accuracy, and imperfect evaluation methods. Firstly, the boundary algorithm system is introduced in the research, and the Field Programmable Gate Array (FPGA) by deep learning (DL) is used to accelerate the server hardware network platform and equipped with pan tilt zoom (PTZ) and manage multiple AI + embedded visual boundary algorithm devices. Secondly, the network platform can manage the PTZ and focal length of Internet protocol (IP) cameras, measure, and capture face images, transmit data, and recognize students’ face, head, and body postures. Finally, classroom teaching is evaluated, and students’ behavioral data and functions are designed, debugged, and tested. The research results demonstrate that the method overcomes the problem of high system cost through edge computing and hardware structure, and DL technology is used to overcome the problem of low accuracy of classroom teaching evaluation. Various indicators such as attendance rate, concentration, activity, and richness of teaching links in classroom teaching are obtained. The method involved can make an objective evaluation of classroom teaching and overcome the problem of incomplete classroom teaching evaluation.

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Section: Design and Research Of Classroom Detection System Based On DLmentioning

confidence: 99%

Analysis of the Current Situation of Teaching and Learning of Ideological and Political Theory Courses by Deep Learning

Chen

Zhang

2022

Computational Intelligence and Neuroscience

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“…Xiao et al 28 developed an image pyramid convolutional neural network (IPCNN) model to detect. IPCNN combines image pyramids and deep CNNs to extract features for defect detection, and fusion processing and classification of the extracted features with the FPN.…”

Section: Related Workmentioning

confidence: 99%

Improved single shot multibox detector target detection method based on deep feature fusion

Bai

Sun

Tao

et al. 2021

Concurrency and Computation

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The feature layers of different layers in the single shot multibox detector (SSD) are independently used as the input of the classification network, so it is easy to detect the same object. This article proposes an improved SSD model based on deep feature fusion. In the SSD algorithm, the deep feature fusion between the target detection layer and its adjacent feature layer is used, including convolution kernels and pooling kernels of different sizes, down-sampling of low-level features and up-sampling of deconvolution of high-level features. The network is improved by combining the target frame recommendation strategy in the SSD algorithm and the frame regression algorithm. The experimental results show that the improved SSD algorithm improves the detection accuracy and detection rate of the target, and the effect is more obvious for the relatively small-scale target.

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“…Lin et al [4] constructed a multi-scale cascaded CNN based on MobileNetV2, with a reduced number of parameters thanks to the use of a lightweight backbone. Xiao et al [5] proposed a method for surface defect detection based on Mask R-CNN and image pyramid. It fuses the image and feature pyramids, which improves the detection effect of multi-scale objects.…”

Section: Related Workmentioning

confidence: 99%

Surface Defect Detection in Sanitary Ceramics Based on Lightweight Object Detection Network

Hang

Sun

Yu³

et al. 2022

IEEE Open J. Ind. Electron. Soc.

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Sanitary ceramic products, such as toilet and wash basin, are widely used in our daily life. Sanitary ceramics are expected to have some excellent physical properties, such as corrosion resistance, easy cleaning, and low water absorption. However, surface defects in sanitary ceramics are inevitable due to complex production processes and changing production environment. Therefore, surface defect detection must be performed in the manufacturing process of sanitary ceramics. There are many types of surface defects in sanitary ceramics, and different types of defects have large differences in characteristics and scales. Traditional detection methods with artificially designed features and classifiers are difficult to apply in this context. In addition, there are few studies on surface defect detection methods of sanitary ceramics based on deep neural networks. In this article, a lightweight real-time defect detection network based on the lightweight backbone MobileNetV3 is presented. The proposed network achieves multi-scale detection of surface defects in sanitary ceramics with a multi-layer feature pyramid. Combining region proposal network and anchor-free method, realtime defect detection is achieved. Finally, a detection head with channel attention structure and a low-level mixed feature classification strategy is used to perform defect classification with higher accuracy. Experimental results show that the proposed approach achieves at least 22.9% detection speed improvement and 35.0% average precision improvement while reducing memory consumption by at least 8.4% compared with the classic onestage SSD, YOLO V3 and two-stage Faster R-CNN methods.

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Surface Defect Detection Using Image Pyramid

Cited by 66 publications

References 40 publications

Analysis of the Current Situation of Teaching and Learning of Ideological and Political Theory Courses by Deep Learning

Analysis of the Current Situation of Teaching and Learning of Ideological and Political Theory Courses by Deep Learning

Improved single shot multibox detector target detection method based on deep feature fusion

Surface Defect Detection in Sanitary Ceramics Based on Lightweight Object Detection Network

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