.png){kind=icon}
返回
A Deep Learning-Based Surface Defect Inspection System Using Multiscale and Channel-Compressed Features
.png){kind=icon}
OA
AI
DOI：10.1109/TIM.2020.2986875.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In machine vision-based surface inspection tasks, defects are typically considered as local anomalies in homogeneous background. However, industrial workpieces commonly contain complex structures, including hallow regions, welding joints, or rivet holes. Such obvious structural interference will inevitably cause a cluttered background and mislead the classification results. Moreover, the sizes of various surface defects might change significantly. Last but not least, it is extremely time-consuming and not scalable to capture large-scale defect data sets to train deep CNN models. To address the challenges mentioned earlier, we first proposed to incorporate multiple convolutional layers with different kernel sizes to increase the receptive field and to generate multiscale features. As a result, the proposed model can better handle the cluttered background and defects of various sizes. Also, we purposely compress the size of parameters in the newly added convolutional layers for better learning of defect-related features using a limited number of training samples. Evaluated in a newly constructed surface defect data set (images contain complex structures and defects of various sizes), our proposed model achieves more accurate recognition results compared with the state-of-the-art surface defect classifiers. Moreover, it is a lightweight model and can deliver real-time processing speed (>100 frames/s) on a computer equipped with a single NVIDIA TITAN X Graphics Processing Unit (12-GB memory).
Keyword:
Inspection
Feature extraction
Task analysis
Universal Serial Bus
Training
Computational modeling
Steel
Cluttered background
convolutional neural network (CNN)
defect classification
feature extraction
multireceptive field (MRF)
surface inspection
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.9
论文数:
2.0W
被引数:
5.8W
机构
引用论文
Telehealth use in emergency care during coronavirus disease 2019: a systematic review.png)
JACEP OpenOAAI
JACEP OpenIF0
OAAI分享收藏Nanocrystalline silicon films as multifunctional material for optoelectronic and photovoltaic applications纳米硅薄膜作为光电和光伏应用的多功能材料
PREAI
分享收藏An End-to-End Steel Surface Defect Detection Approach via Fusing Multiple Hierarchical Features融合多个层次特征的端到端钢表面缺陷检测方法OAAI
OAAI分享收藏
查看更多内容