International Conference on Quality Control by Artificial Vision最新文献

Reducing the latency and size of a deep CNN model for surface defect detection in manufacturing 减少制造过程中表面缺陷检测的深度CNN模型的延迟和尺寸

International Conference on Quality Control by Artificial Vision Pub Date : 2023-07-28 DOI: 10.1117/12.2692962

N. Pizurica, Kosta Pavlovic, Slavko Kovacevic, Igor Jovančević

引用次数: 0

The evolution of the non-destructive defect detection in composites with the use of terahertz radiation 太赫兹辐射在复合材料无损检测中的应用进展

International Conference on Quality Control by Artificial Vision Pub Date : 2023-07-28 DOI: 10.1117/12.2690724

M. Strąg, W. Świderski

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Inspection of mechanical assemblies based on 3D deep learning segmentation 基于三维深度学习分割的机械装配件检测

International Conference on Quality Control by Artificial Vision Pub Date : 2023-07-28 DOI: 10.1117/12.2692569

Assya Boughrara, Igor Jovančević, J. Orteu, Mathieu Belloc

{"title":"Inspection of mechanical assemblies based on 3D deep learning segmentation","authors":"Assya Boughrara, Igor Jovančević, J. Orteu, Mathieu Belloc","doi":"10.1117/12.2692569","DOIUrl":"https://doi.org/10.1117/12.2692569","url":null,"abstract":"We are focused on conformity control of complex aeronautical mechanical assemblies, typically an aircraft engine at the end or in the middle of the assembly process. Our overall system should ensure that all the mechanical parts are present and well-mounted. A 3D scanner carried by a robot arm provides acquisitions of 3D point clouds which are further processed. Computer-Aided Design (CAD) model of the mechanical assembly is available. In this paper, we are concentrating on detecting the absence of mechanical elements. Previously we have developed a rendering pipeline for creating realistic synthetic 3D point cloud data. We do this by using the CAD model and taking into account occlusion and self-occlusion of mechanical parts. In this paper, an existing deep neural network for 3D segmentation is experimentally chosen and trained on these synthetic data. Further, the model is evaluated on real data acquired by a 3D scanner and has shown good quantitative results according to a segmentation metric. Finally, when a threshold is applied to the segmentation result, a final decision is made on the absence/presence problem. The achieved accuracy is 98.7%. Our research work is being carried out within the framework of the joint research laboratory ”Inspection 4.0” between IMT Mines Albi/ICA and the company Diota specialized in the development of numerical tools for Industry 4.0. This research is a continuation of the work presented at the QCAV’2021 conference [1].","PeriodicalId":295011,"journal":{"name":"International Conference on Quality Control by Artificial Vision","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125594748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Image processing through deep learning after DI extraction for the SHM of aeronautic composite structures using Lamb waves 基于Lamb波的航空复合材料结构SHM提取后的深度学习图像处理

International Conference on Quality Control by Artificial Vision Pub Date : 2023-07-28 DOI: 10.1117/12.2692632

Salman Husain, M. Rébillat, F. Ababsa

{"title":"Image processing through deep learning after DI extraction for the SHM of aeronautic composite structures using Lamb waves","authors":"Salman Husain, M. Rébillat, F. Ababsa","doi":"10.1117/12.2692632","DOIUrl":"https://doi.org/10.1117/12.2692632","url":null,"abstract":"Structural health monitoring (SHM) is a crucial process that enables the diagnosis of the health state of civil and industrial smart structures through autonomous and in-situ non-destructive measurements. The focus of our study is on the damage classification step within the aeronautic context, where the primary objective is to distinguish between different damage types in composite plates. To achieve this, we considered three experimental damages - impact, delamination, and magnet - on an aeronautic composite plate embedded with a piezoelectric array and excited it using ultrasonic guided Lamb waves. We recorded signals resulting from pristine and damaged states and used three methods to create images from the raw recorded data. These methods employed Damage Indexes (DI) that compare signals in the healthy and damaged states for each actuator/sensor path. For the first two methods, images were directly created as pixel maps depicting DI distribution according to the actuator/receiver pairs over the plate. The last method applied the classical RAPID damage localization algorithm, generating damage localization maps associated with a given DI. The datasets generated by the two methods were fed into a Convolutional Neural Network (CNN) for damage classification purposes. Our study demonstrated that the best accuracy for the introduced methods was above 92% for different hyperparameters configurations, indicating their ability to perform the desired SHM damage classification task. The DI-based approach was much more efficient than the RAPID-based method, which was not intuitively expected. These findings contribute to the development of effective SHM techniques for aeronautic composite plates, paving the way for further improvements in this critical field.","PeriodicalId":295011,"journal":{"name":"International Conference on Quality Control by Artificial Vision","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122506980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Three-dimensional temperature distribution mapping by generative adversarial network in low light environment using thermography 生成对抗网络在弱光环境下的三维温度分布映射

International Conference on Quality Control by Artificial Vision Pub Date : 2023-07-28 DOI: 10.1117/12.3000051

Shohei Oka, Yonghoon Ji, Hiromitsu Fujii, H. Kono

引用次数: 0

Laser flying-spot thermography: an open-access dataset for machine learning and deep learning 激光飞点热成像:用于机器学习和深度学习的开放获取数据集

International Conference on Quality Control by Artificial Vision Pub Date : 2023-07-28 DOI: 10.1117/12.3000481

Kevin Helvig, P. Trouvé-Peloux, L. Gavérina, J. Roche, Baptiste Abeloos, C. Pradère

{"title":"Laser flying-spot thermography: an open-access dataset for machine learning and deep learning","authors":"Kevin Helvig, P. Trouvé-Peloux, L. Gavérina, J. Roche, Baptiste Abeloos, C. Pradère","doi":"10.1117/12.3000481","DOIUrl":"https://doi.org/10.1117/12.3000481","url":null,"abstract":"“Flying spot” laser infrared thermography (FST) is a non destructive testing technique able to detect small defects by scanning surfaces with a laser heat source. Defects, such as cracks on metallic parts, are revealed by the disturbance of heat propagation measured by an infrared camera. Deep learning approaches are now very efficient to automatically analyse and use contextual information from data, and can be used for crack detection. However, in the literature only few works deal with the use of deep learning for the crack detection in FST. Indeed obtaining a large amount of data from FST examinations can be expensive and time-consuming. We propose here to build a generic, open-access dataset of laser thermography for defect detection. This database can be used by the community to develop new crack detection methods that can be benchmarked on the same database, as well as for pretraining networks for similar application tasks. We also present results of state of the art detection networks trained with the proposed database. These models give a basis for future works. Dataset, called FLYD (FLYing spot thermography Dataset), will be available in : https://github.com/kevinhelvig/FLYD/.","PeriodicalId":295011,"journal":{"name":"International Conference on Quality Control by Artificial Vision","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128984858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance comparison of division of time and division of focal plan polarimeters 分时和分焦平面偏振计的性能比较

International Conference on Quality Control by Artificial Vision Pub Date : 2023-07-28 DOI: 10.1117/12.2688566

Pierre-Jean Lapray, L. Bigué

引用次数: 0

Underwater SLAM based on object recognition using YOLO in acoustic images 基于YOLO声学图像目标识别的水下SLAM

International Conference on Quality Control by Artificial Vision Pub Date : 2023-07-28 DOI: 10.1117/12.3000053

Hiroki Nakamura, Takahiro Nonoda, Yonghoon Ji

引用次数: 0

f-AnoGAN for non-destructive testing in industrial anomaly detection 用于工业异常检测的无损检测

International Conference on Quality Control by Artificial Vision Pub Date : 2023-07-28 DOI: 10.1117/12.3000063

Oumaima Sliti, M. Devanne, S. Kohler, N. Samet, Jonathan Weber, C. Cudel

{"title":"f-AnoGAN for non-destructive testing in industrial anomaly detection","authors":"Oumaima Sliti, M. Devanne, S. Kohler, N. Samet, Jonathan Weber, C. Cudel","doi":"10.1117/12.3000063","DOIUrl":"https://doi.org/10.1117/12.3000063","url":null,"abstract":"Being able to identify defects is an essential step during manufacturing processes. Yet, not all defects are necessarily known and sufficiently well described in the databases images. The challenge we address in this paper is to detect any defect by fitting a model using only normal samples of industrial parts. For this purpose, we propose to test fast AnoGAN (f-AnoGAN) approach based on a generative adversarial network (GAN). The method is an unsupervised learning algorithm, that contains two phases; first, we train a generative model using only normal images, which proposes a fast mapping of new data into the latent space. Second, we add and train an encoder to reconstruct images. The anomaly detection is defined by the reconstruction error between the defected data and the reconstructed ones, and the residual error of the discriminator. For our experiments, we use two sets of industrial data; the MVTec Anomaly Detection Dataset and a private dataset which is based on thermal-wave and used for non-destructive testing. This technique has been utilized in research for the evaluation of industrial materials. Applying the f-AnoGAN in this domain offers high anomaly detection accuracy.","PeriodicalId":295011,"journal":{"name":"International Conference on Quality Control by Artificial Vision","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114742368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Visuo-tactile pose tracking method for in-hand robot manipulation tasks of quotidian objects 手持机器人处理日常物体任务的视触觉姿态跟踪方法

International Conference on Quality Control by Artificial Vision Pub Date : 2023-07-28 DOI: 10.1117/12.2690812

Camille Taglione, C. Mateo, C. Stolz

引用次数: 0