AOI Techniques for Surface Defect Inspection

2010 ◽  
Vol 36 ◽  
pp. 297-302 ◽  
Author(s):  
Rong Sheng Lu ◽  
Yan Qiong Shi ◽  
Qi Li ◽  
Qing Ping Yu
Keyword(s):  
Rapid Development ◽  
Light Illumination ◽  
Defect Inspection ◽  
Emerging Industries ◽  
Optical Inspection ◽  
Textile Printing ◽  
Automobile Steel ◽  
Automated Optical Inspection ◽  
Key Techniques

Recent years, automated optical inspection (AOI) is developed very fast along with the rapid development of the emerging industries of semiconductor, LCD, PCB, optical communication and precision assembly, and also widely used in the industries of robot, automobile, steel, textile, printing, medicine, etc. In this paper, we will take a review of the AOI techniques, which are used for defect inspection on a large surface, such as inspecting the quality of TFT-LCD glass substrate and filter. The AOI system architecture having high inspection speed is illustrated. Some key techniques of light illumination, distributed image processing and convey mechanism, are explained.

scholarly journals Sandblasting Defect Inspection of Investment Castings Based on Deep Convolutional Neural Network

2021 ◽  
Author(s):  
Jenn-Kun Kuo ◽  
Jun-Jia Wu ◽  
Pei-Hsing Huang ◽  
Chin-Yi Cheng
Keyword(s):  
Prediction Accuracy ◽  
Direct Detection ◽  
Health And Safety ◽  
Defect Inspection ◽  
Labor Costs ◽  
High Noise ◽  
Learning Framework ◽  
Working Environments ◽  
Optical Inspection ◽  
Automated Optical Inspection

Abstract Investment castings often have surface impurities and pieces of shell molds can remain on the surface after sandblasting. Identification of defects involves time-consuming manual inspections in working environments of high noise and poor air quality. To reduce labor costs and increase the health and safety of employees, we applied automated optical inspection (AOI) combined with a deep learning framework based on convolutional neural networks (CNNs) to the detection of sandblasting defects. We applied the following four classic CNN models for training and predictive classification: AlexNet, VGG-16, GoogLeNet, and ResNet-34. In terms of predictive classification, AlexNet, VGG-16, and GoogLeNet v1 could accurately determine whether there were defects. Among the four models, AlexNet was the most accurate, with prediction accuracy of 99.53% for qualifying products and 100% for defective products. We demonstrate a direct detection technique based on the AOI and CNN structure with a fast and flexible computational interface.

scholarly journals Dual-Kernel-Based Aggregated Residual Network for Surface Defect Inspection in Injection Molding Processes

2020 ◽  
Vol 10 (22) ◽  
pp. 8171
Author(s):  
Hwaseop Lee ◽  
Kwangyeol Ryu
Keyword(s):  
Injection Molding ◽  
Surface Defect ◽  
Defect Inspection ◽  
Residual Network ◽  
Cycle Times ◽  
Optical Inspection ◽  
Quality Product ◽  
Weakly Supervised

Automated quality inspection has been receiving increasing attention in manufacturing processes. Since the introduction of convolutional neural networks (CNNs), many researchers have attempted to apply CNNs to classification and detection of defect images. However, injection molding processes have not received much attention in this field of research because of product diversity, difficulty in obtaining uniform-quality product images, and short cycle times. In this study, two types of dual-kernel-based aggregated residual networks are proposed by utilizing a fixed kernel and a deformable kernel to detect surface and shape defects of molded products. The aggregated residual network is selected as a backbone, and a fixed-size, deformable kernel is applied for extracting surface and geometric features simultaneously. Comparative studies are conducted by including the existing research using the Weakly Supervised Learning for Industrial Optical Inspection dataset, which is a DAGM dataset. A case study reveals that the proposed method is applicable for inspecting the quality of injection molding products with excellent performance.

A New Approach to Enhance Artificial Intelligence for Robot Picking System Using Auto Picking Point Annotation

2021 ◽  
Author(s):  
Cheng-Han (Lance) Tsai ◽  
Jen-Yuan (James) Chang
Keyword(s):  
Artificial Intelligence ◽  
Synthetic Data ◽  
Real Data ◽  
Realistic Model ◽  
New Approach ◽  
Optical Inspection ◽  
Object Locations ◽  
Automated Optical Inspection ◽  
Occlusion Effect

Abstract Artificial Intelligence (AI) has been widely used in different domains such as self-driving, automated optical inspection, and detection of object locations for the robotic pick and place operations. Although the current results of using AI in the mentioned fields are good, the biggest bottleneck for AI is the need for a vast amount of data and labeling of the corresponding answers for a sufficient training. Evidentially, these efforts still require significant manpower. If the quality of the labelling is unstable, the trained AI model becomes unstable and as consequence, so do the results. To resolve this issue, the auto annotation system is proposed in this paper with methods including (1) highly realistic model generation with real texture, (2) domain randomization algorithm in the simulator to automatically generate abundant and diverse images, and (3) visibility tracking algorithm to calculate the occlusion effect objects cause on each other for different picking strategy labels. From our experiments, we will show 10,000 images can be generated per hour, each having multiple objects and each object being labelled in different classes based on their visibility. Instance segmentation AI models can also be trained with these methods to verify the gaps between performance synthetic data for training and real data for testing, indicating that even at mAP 70 the mean average precision can reach 70%!

Perilaku Birokrat Dalam Pelayanan Publik Di Distrik Asologaima Kabupaten Jayawijaya

2020 ◽  
Vol 12 (2) ◽  
pp. 93-100
Author(s):  
Yoel Tabuni
Keyword(s):  
Consumer Satisfaction ◽  
Rapid Development ◽  
Main Task ◽  
Government Administration ◽  
Following Instructions ◽  
District Government ◽  
The Times ◽  
Rule Governance ◽  
Descriptive Method

In line with the rapid development of the times and the increasingly complex problems faced by the state, there has also been a development in government administration which has been marked by a shift in the paradigm of governance from Rule Governance. This situation makes the bureaucracy rigid, in an environment that is only limited to flowing the instructions or following instructions. The district government in an Asologaima District has the main task of carrying out part of the authority delegated by the district head in the fields of government, economy, and development, society, peace, and order as well as coordination.The method is sed is descriptive method. Bureaucrats as providers of public services must be able to provide quality services, the quality of service of bureaucrats to society is closely related to customer satisfaction or consumer satisfaction as the recipient of the service itself.

scholarly journals Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility

Smart Cities ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 894-918
Author(s):  
Luís Rosa ◽  
Fábio Silva ◽  
Cesar Analide
Keyword(s):  
Internet Of Things ◽  
Mobile Networks ◽  
Smart Cities ◽  
Human Mobility ◽  
Rapid Development ◽  
Disruptive Technology ◽  
New Paradigm ◽  
Design And Optimization ◽  
High Level

The evolution of Mobile Networks and Internet of Things (IoT) architectures allows one to rethink the way smart cities infrastructures are designed and managed, and solve a number of problems in terms of human mobility. The territories that adopt the sensoring era can take advantage of this disruptive technology to improve the quality of mobility of their citizens and the rationalization of their resources. However, with this rapid development of smart terminals and infrastructures, as well as the proliferation of diversified applications, even current networks may not be able to completely meet quickly rising human mobility demands. Thus, they are facing many challenges and to cope with these challenges, different standards and projects have been proposed so far. Accordingly, Artificial Intelligence (AI) has been utilized as a new paradigm for the design and optimization of mobile networks with a high level of intelligence. The objective of this work is to identify and discuss the challenges of mobile networks, alongside IoT and AI, to characterize smart human mobility and to discuss some workable solutions to these challenges. Finally, based on this discussion, we propose paths for future smart human mobility researches.

scholarly journals Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4613
Author(s):  
Yi-Cheng Mao ◽  
Tsung-Yi Chen ◽  
He-Sheng Chou ◽  
Szu-Yin Lin ◽  
Sheng-Yu Liu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Rapid Development ◽  
Radiographic Examination ◽  
Analysis Model ◽  
Common Procedure ◽  
Medical Treatments ◽  
Pass Filter ◽  
Single Tooth ◽  
Imaging Research

Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early, the treatment will be relatively easy, which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However, the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu’s threshold image enhancement technology, this research solves the problem that the original cutting technology cannot extract certain single teeth, and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN), which can identify caries and restorations from the bitewing images. Moreover, it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image, which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization, (2) a dental image cropping procedure to obtain individually separated tooth samples, and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks, namely, AlexNet, GoogleNet, Vgg19, and ResNet50, experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%, respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film.

Professional Psychology in Portugal

1997 ◽  
Vol 81 (3_suppl) ◽  
pp. 1211-1222 ◽  
Author(s):  
Teresa Fagulha ◽  
Richard H. Dana
Keyword(s):  
Rapid Development ◽  
Well Being ◽  
Professional Psychology ◽  
Current Status ◽  
Environmental Concerns ◽  
Advanced Degrees ◽  
Professional Degree ◽  
History Of ◽  
Training Research

This paper describes the history and current status of professional psychology in Portugal where a unique perspective combines training, research, and practical contributions from Europe and the Americas with their own history of psychological tradition and expertise. Training in professional psychology includes Social Psychology and Educational and Vocational Guidance specializations in addition to Clinical Psychology and Psychotherapy and Counseling for the professional degree, Licenciatura. Advanced degrees are offered in Environmental Psychology, Career Development, Social Cognition, and other areas, primarily for academic positions. Research in all of these areas is expected to have applied outcomes that contribute to individual well being and an improved quality of life for the entire population. The result has been a rapid development of an indigenous professional psychology to address mental health, social, and environmental concerns that compel psychological attention and resources worldwide as well as those problems of local and national origins.

scholarly journals Ceramic Cracks Segmentation with Deep Learning

2021 ◽  
Vol 11 (13) ◽  
pp. 6017
Author(s):  
Gerivan Santos Junior ◽  
Janderson Ferreira ◽  
Cristian Millán-Arias ◽  
Ramiro Daniel ◽  
Alberto Casado Junior ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ceramic Plate ◽  
Ceramic Tiles ◽  
Entire Area ◽  
Manual Inspection ◽  
Optical Inspection ◽  
Proposed Model ◽  
Processing Step ◽  
Long Time ◽  
Automated Optical Inspection

Cracks are pathologies whose appearance in ceramic tiles can cause various damages due to the coating system losing water tightness and impermeability functions. Besides, the detachment of a ceramic plate, exposing the building structure, can still reach people who move around the building. Manual inspection is the most common method for addressing this problem. However, it depends on the knowledge and experience of those who perform the analysis and demands a long time and a high cost to map the entire area. This work focuses on automated optical inspection to find faults in ceramic tiles performing the segmentation of cracks in ceramic images using deep learning to segment these defects. We propose an architecture for segmenting cracks in facades with Deep Learning that includes an image pre-processing step. We also propose the Ceramic Crack Database, a set of images to segment defects in ceramic tiles. The proposed model can adequately identify the crack even when it is close to or within the grout.

scholarly journals Intelligent Routing Control for MANET Based on Reinforcement Learning

2018 ◽  
Vol 232 ◽  
pp. 04002
Author(s):  
Fang Dong ◽  
Ou Li ◽  
Min Tong
Keyword(s):  
Reinforcement Learning ◽  
Control Algorithm ◽  
Rapid Development ◽  
Selection Strategy ◽  
Performance Simulation ◽  
Multiple Parameters ◽  
Intelligent Routing ◽  
Routing Control ◽  
The Cost

With the rapid development and wide use of MANET, the quality of service for various businesses is much higher than before. Aiming at the adaptive routing control with multiple parameters for universal scenes, we propose an intelligent routing control algorithm for MANET based on reinforcement learning, which can constantly optimize the node selection strategy through the interaction with the environment and converge to the optimal transmission paths gradually. There is no need to update the network state frequently, which can save the cost of routing maintenance while improving the transmission performance. Simulation results show that, compared with other algorithms, the proposed approach can choose appropriate paths under constraint conditions, and can obtain better optimization objective.

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