Multimodal fusion convolutional neural network with cross-attention mechanism for internal defect detection of magnetic tile

H Lu, Y Zhu, M Yin, G Yin, L Xie - IEEE Access, 2022 - ieeexplore.ieee.org
The internal defect detection of magnetic tile is extremely significant before mounting.
Currently, this task is completely realized by manual operation in the magnetic tile …

FFCNN: A deep neural network for surface defect detection of magnetic tile

L Xie, X Xiang, H Xu, L Wang, L Lin… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Surface quality assessment of magnetic tile before mounting is extremely significant. At
present, this task is mainly accomplished by experienced workers in industry, which exposes …

Fully convolutional cross-scale-flows for image-based defect detection

M Rudolph, T Wehrbein… - Proceedings of the …, 2022 - openaccess.thecvf.com
In industrial manufacturing processes, errors frequently occur at unpredictable times and in
unknown manifestations. We tackle this problem, known as automatic defect detection …

TAS2-Net: Triple-Attention Semantic Segmentation Network for Small Surface Defect Detection

T Liu, Z He - IEEE Transactions on Instrumentation and …, 2022 - ieeexplore.ieee.org
Surface defect detection plays a vital role in manufacturing. However, it remains challenging:
1) small samples due to fewer defective products relative to numerous normal products and …

[HTML][HTML] Deep learning approaches on defect detection in high resolution aerial images of insulators

Q Wen, Z Luo, R Chen, Y Yang, G Li - Sensors, 2021 - mdpi.com
By detecting the defect location in high-resolution insulator images collected by unmanned
aerial vehicle (UAV) in various environments, the occurrence of power failure can be timely …

Same same but differnet: Semi-supervised defect detection with normalizing flows

M Rudolph, B Wandt… - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
The detection of manufacturing errors is crucial in fabrication processes to ensure product
quality and safety standards. Since many defects occur very rarely and their characteristics …

A light-weighted CNN model for wafer structural defect detection

X Chen, J Chen, X Han, C Zhao, D Zhang, K Zhu… - IEEE …, 2020 - ieeexplore.ieee.org
Silicon wafer is the raw material of semiconductor chip. It is important and challenging to
research a fast and accurate method of identifying and classifying wafer structural defects …

DefGAN: Defect detection GANs with latent space pitting for high-speed railway insulator

D Zhang, S Gao, L Yu, G Kang, X Wei… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
As a key component of the high-speed railway catenary, the insulator plays an important role
in supporting the catenary and maintaining the insulation between the catenary and earth …

[HTML][HTML] An improved CenterNet model for insulator defect detection using aerial imagery

H Xia, B Yang, Y Li, B Wang - Sensors, 2022 - mdpi.com
For the issue of low accuracy and poor real-time performance of insulator and defect
detection by an unmanned aerial vehicle (UAV) in the process of power inspection, an …

Kd-lightnet: A lightweight network based on knowledge distillation for industrial defect detection

J Liu, H Li, F Zuo, Z Zhao, S Lu - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
At present, the method based on deep learning performs well in public object detection
tasks. However, there are still two problems to be solved for industrial defect detection: 1) …