Robust Fabric Defects Inspection System Using Deep Learning Architecture

2021 ◽  
Vol 50 (1) ◽  
pp. 20200778
Author(s):  
T. Shanthi ◽  
M. E. Paramasivam ◽  
C. Prakash ◽  
K. Manju ◽  
Eldho Paul ◽  
...  
Keyword(s):  
Deep Learning ◽  
Inspection System ◽  
Defects Inspection

scholarly journals A Deep-Learning-based 3D Defect Quantitative Inspection System in CC Products Surface

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 980 ◽  
Author(s):  
Liming Zhao ◽  
Fangfang Li ◽  
Yi Zhang ◽  
Xiaodong Xu ◽  
Hong Xiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Surface Region ◽  
Inspection System ◽  
Scanning System ◽  
Inspection Method ◽  
Scanning Method ◽  
Image Scanning ◽  
Defects Inspection ◽  
Inspection Strategy ◽  
Defects Classification

To create an intelligent surface region of interests (ROI) 3D quantitative inspection strategy a reality in the continuous casting (CC) production line, an improved 3D laser image scanning system (3D-LDS) was established based on binocular imaging and deep-learning techniques. In 3D-LDS, firstly, to meet the requirements of the industrial application, the CCD laser image scanning method was optimized in high-temperature experiments and secondly, we proposed a novel region proposal method based on 3D ROI initial depth location for effectively suppressing redundant candidate bounding boxes generated by pseudo-defects in a real-time inspection process. Thirdly, a novel two-step defects inspection strategy was presented by devising a fusion deep CNN model which combined fully connected networks (for defects classification/recognition) and fully convolutional networks (for defects delineation). The 3D-LDS’ dichotomous inspection method of defects classification and delineation processes are helpful in understanding and addressing challenges for defects inspection in CC product surfaces. The applicability of the presented methods is mainly tied to the surface quality inspection for slab, strip and billet products.

scholarly journals Product Inspection Methodology via Deep Learning: An Overview

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5039
Author(s):  
Tae-Hyun Kim ◽  
Hye-Rin Kim ◽  
Yeong-Jun Cho
Keyword(s):  
Deep Learning ◽  
Product Quality ◽  
Inspection System ◽  
Quality Inspection ◽  
Learning Models ◽  
Unified Framework ◽  
Learning Techniques ◽  
Inspection Systems ◽  
System Maintenance ◽  
Test Scenarios

In this study, we present a framework for product quality inspection based on deep learning techniques. First, we categorize several deep learning models that can be applied to product inspection systems. In addition, we explain the steps for building a deep-learning-based inspection system in detail. Second, we address connection schemes that efficiently link deep learning models to product inspection systems. Finally, we propose an effective method that can maintain and enhance a product inspection system according to improvement goals of the existing product inspection systems. The proposed system is observed to possess good system maintenance and stability owing to the proposed methods. All the proposed methods are integrated into a unified framework and we provide detailed explanations of each proposed method. In order to verify the effectiveness of the proposed system, we compare and analyze the performance of the methods in various test scenarios. We expect that our study will provide useful guidelines to readers who desire to implement deep-learning-based systems for product inspection.

Pipe morphology defects inspection system with active stereo omnidirectional vision sensor

2016 ◽  
Vol 45 (11) ◽  
pp. 1117005
Author(s):  
汤一平 Tang Yiping ◽  
鲁少辉 Lu Shaohui ◽  
吴 挺 Wu Ting ◽  
韩国栋 Han Guodong
Keyword(s):  
Inspection System ◽  
Omnidirectional Vision ◽  
Vision Sensor ◽  
Defects Inspection ◽  
Active Stereo

Automated defect inspection system for metal surfaces based on deep learning and data augmentation

2020 ◽  
Vol 55 ◽  
pp. 317-324 ◽  
Author(s):  
Jong Pil Yun ◽  
Woosang Crino Shin ◽  
Gyogwon Koo ◽  
Min Su Kim ◽  
Chungki Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Metal Surfaces ◽  
Data Augmentation ◽  
Inspection System ◽  
Defect Inspection

scholarly journals Intelligent System for Railway Wheelset Press-Fit Inspection Using Deep Learning

2021 ◽  
Vol 11 (17) ◽  
pp. 8243
Author(s):  
Jung-Sing Jwo ◽  
Ching-Sheng Lin ◽  
Cheng-Hsiung Lee ◽  
Li Zhang ◽  
Sin-Ming Huang
Keyword(s):  
Deep Learning ◽  
Intelligent System ◽  
Working Hours ◽  
Inspection System ◽  
Practical Case ◽  
Assembly Quality ◽  
Press Fit ◽  
Manual Inspection ◽  
Testing Dataset ◽  
Traditional Manufacturing

Railway wheelsets are the key to ensuring the safe operation of trains. To achieve zero-defect production, railway equipment manufacturers must strictly control every link in the wheelset production process. The press-fit curve output by the wheelset assembly machine is an essential indicator of the wheelset’s assembly quality. The operators will still need to manually and individually recheck press-fit curves in our practical case. However, there are many uncertainties in the manual inspection. For example, subjective judgment can easily cause inconsistent judgment results between different inspectors, or the probability of human misinterpretation can increase as the working hours increase. Therefore, this study proposes an intelligent railway wheelset inspection system based on deep learning, which improves the reliability and efficiency of manual inspection of wheelset assembly quality. To solve the severe imbalance in the number of collected images, this study establishes a predicted model of press-fit quality based on a deep Siamese network. Our experimental results show that the precision measurement is outstanding for the testing dataset contained 3863 qualified images and 28 unqualified images of press-fit curves. The proposed system will serve as a successful case of a paradigm shift from traditional manufacturing to digital manufacturing.

scholarly journals Offline Deep-learning-based Defective Track Fastener Detection and Inspection System

2020 ◽  
Vol 32 (10) ◽  
pp. 3429
Author(s):  
Chen-Chiung Hsieh ◽  
Ya-Wen Lin ◽  
Li-Hung Tsai ◽  
Wei-Hsin Huang ◽  
Shang-Lin Hsieh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Inspection System
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