scholarly journals AI Landing for Sheet Metal-Based Drawer Box Defect Detection Using Deep Learning (ALDB-DL)

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 768
Author(s):  
Ruey-Kai Sheu ◽  
Lun-Chi Chen ◽  
Mayuresh Sunil Pardeshi ◽  
Kai-Chih Pai ◽  
Chia-Yu Chen
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Sheet Metal ◽  
Defect Detection ◽  
Process Integration ◽  
Learning Algorithm ◽  
Industrial Process ◽  
Orange Peel ◽  
Image Feature ◽  
Discussion Section

Sheet metal-based products serve as a major portion of the furniture market and maintain higher quality standards by being competitive. During industrial processes, while converting a sheet metal to an end product, new defects are observed and thus need to be identified carefully. Recent studies have shown scratches, bumps, and pollution/dust are identified, but orange peel defects present overall a new challenge. So our model identifies scratches, bumps, and dust by using computer vision algorithms, whereas orange peel defect detection with deep learning have a better performance. The goal of this paper was to resolve artificial intelligence (AI) as an AI landing challenge faced in identifying various kinds of sheet metal-based product defects by ALDB-DL process automation. Therefore, our system model consists of multiple cameras from two different angles to capture the defects of the sheet metal-based drawer box. The aim of this paper was to solve multiple defects detection as design and implementation of Industrial process integration with AI by Automated Optical Inspection (AOI) for sheet metal-based drawer box defect detection, stated as AI Landing for sheet metal-based Drawer Box defect detection using Deep Learning (ALDB-DL). Therefore, the scope was given as achieving higher accuracy using multi-camera-based image feature extraction using computer vision and deep learning algorithm for defect classification in AOI. We used SHapley Additive exPlanations (SHAP) values for pre-processing, LeNet with a (1 × 1) convolution filter, and a Global Average Pooling (GAP) Convolutional Neural Network (CNN) algorithm to achieve the best results. It has applications for sheet metal-based product industries with improvised quality control for edge and surface detection. The results were competitive as the precision, recall, and area under the curve were 1.00, 0.99, and 0.98, respectively. Successively, the discussion section presents a detailed insight view about the industrial functioning with ALDB-DL experience sharing.

scholarly journals Application of Data-Driven Iterative Learning Algorithm in Transmission Line Defect Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yuquan Chen ◽  
Hongxing Wang ◽  
Jie Shen ◽  
Xingwei Zhang ◽  
Xiaowei Gao
Keyword(s):  
Deep Learning ◽  
Transmission Line ◽  
Target Detection ◽  
Defect Detection ◽  
Learning Algorithm ◽  
Small Sample ◽  
Iterative Learning ◽  
Data Driven ◽  
Learning Technology ◽  
Line Defect

Deep learning technology has received extensive consideration in recent years, and its application value in target detection is also increasing day by day. In order to accelerate the practical process of deep learning technology in electric transmission line defect detection, the current work used the improved Faster R-CNN algorithm to achieve data-driven iterative training and defect detection functions for typical transmission line defect targets. Based on Faster R-CNN, we proposed an improved network that combines deformable convolution and feature pyramid modules and combined it with a data-driven iterative learning algorithm; it achieves extremely automated and intelligent transmission line defect target detection, forming an intelligent closed-loop image processing. The experimental results show that the increase of the recognition of improved Faster R-CNN network combined with data-driven iterative learning algorithm for the pin defect target is 31.7% more than Faster R-CNN. In the future, the proposed method can quickly improve the accuracy of transmission line defect target detection in a small sample and save manpower. It also provides some theoretical guidance for the practical work of transmission line defect target detection.

scholarly journals Segmentation for Complex Background Images using Deep Learning Techniques

2019 ◽  
Vol 8 (2) ◽  
pp. 1746-1750
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Vision System ◽  
Automated Segmentation ◽  
Computer Vision System ◽  
Important Stage ◽  
Deep Learning Algorithm ◽  
Complex Background ◽  
Learning Techniques

Segmentation is an important stage in any computer vision system. Segmentation involves discarding the objects which are not of our interest and extracting only the object of our interest. Automated segmentation has become very difficult when we have complex background and other challenges like illumination, occlusion etc. In this project we are designing an automated segmentation system using deep learning algorithm to segment images with complex background.

scholarly journals Local Deep Descriptor for Remote Sensing Image Feature Matching

2019 ◽  
Vol 11 (4) ◽  
pp. 430 ◽  
Author(s):  
Yunyun Dong ◽  
Weili Jiao ◽  
Tengfei Long ◽  
Lanfa Liu ◽  
Guojin He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Computer Vision ◽  
Deep Learning ◽  
Feature Matching ◽  
Remote Sensing Image ◽  
Image Feature ◽  
Training Dataset ◽  
Feature Descriptor ◽  
Remote Sensing Images

Feature matching via local descriptors is one of the most fundamental problems in many computer vision tasks, as well as in the remote sensing image processing community. For example, in terms of remote sensing image registration based on the feature, feature matching is a vital process to determine the quality of transform model. While in the process of feature matching, the quality of feature descriptor determines the matching result directly. At present, the most commonly used descriptor is hand-crafted by the designer’s expertise or intuition. However, it is hard to cover all the different cases, especially for remote sensing images with nonlinear grayscale deformation. Recently, deep learning shows explosive growth and improves the performance of tasks in various fields, especially in the computer vision community. Here, we created remote sensing image training patch samples, named Invar-Dataset in a novel and automatic way, then trained a deep learning convolutional neural network, named DescNet to generate a robust feature descriptor for feature matching. A special experiment was carried out to illustrate that our created training dataset was more helpful to train a network to generate a good feature descriptor. A qualitative experiment was then performed to show that feature descriptor vector learned by the DescNet could be used to register remote sensing images with large gray scale difference successfully. A quantitative experiment was then carried out to illustrate that the feature vector generated by the DescNet could acquire more matched points than those generated by hand-crafted feature Scale Invariant Feature Transform (SIFT) descriptor and other networks. On average, the matched points acquired by DescNet was almost twice those acquired by other methods. Finally, we analyzed the advantages of our created training dataset Invar-Dataset and DescNet and gave the possible development of training deep descriptor network.

scholarly journals Deep learning—a first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact

2021 ◽  
Vol 7 ◽  
pp. e773
Author(s):  
Jan Egger ◽  
Antonio Pepe ◽  
Christina Gsaxner ◽  
Yuan Jin ◽  
Jianning Li ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Language Processing ◽  
Learning Algorithm ◽  
Research Impact ◽  
Basic Structure ◽  
Search Term ◽  
Learning Networks ◽  
Scientific Disciplines ◽  
Short Period

Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by drawing inspiration from the learning of a human brain. Similar to the basic structure of a brain, which consists of (billions of) neurons and connections between them, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10,000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides already over 11,000 results in Q3 2020 for the search term ‘deep learning’, and around 90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines and outline the research impact that they already have during a short period of time. The categories (computer vision, language processing, medical informatics and additional works) have been chosen according to the underlying data sources (image, language, medical, mixed). In addition, we review the common architectures, methods, pros, cons, evaluations, challenges and future directions for every sub-category.

Machine Vision-based Defect Detection Using Deep Learning Algorithm

2020 ◽  
Vol 40 (1) ◽  
pp. 47-52
Author(s):  
Dae-Hyun Kim ◽  
Seung Bin Boo ◽  
Hyeon Cheol Hong ◽  
Won Gu Yeo ◽  
Nam Yong Lee
Keyword(s):  
Deep Learning ◽  
Machine Vision ◽  
Defect Detection ◽  
Learning Algorithm ◽  
Deep Learning Algorithm

scholarly journals Distributed training strategies for a computer vision deep learning algorithm on a distributed GPU cluster

2017 ◽  
Vol 108 ◽  
pp. 315-324 ◽  
Author(s):  
Víctor Campos ◽  
Francesc Sastre ◽  
Maurici Yagües ◽  
Míriam Bellver ◽  
Xavier Giró-i-Nieto ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Gpu Cluster ◽  
Distributed Training ◽  
Deep Learning Algorithm ◽  
Training Strategies
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