International Conference on Quality Control by Artificial Vision最新文献_第5页

Semi-automatic tools for nanoscale metrology and annotations for deep learning automation on electron microscopy images 用于纳米尺度计量的半自动工具和用于电子显微镜图像深度学习自动化的注释

International Conference on Quality Control by Artificial Vision Pub Date : 2023-07-28 DOI: 10.1117/12.2690493

I. Sanou, J. Baderot, Y. Benezeth, S. Bricq, F. Marzani, S. Martínez, J. Foucher

{"title":"Semi-automatic tools for nanoscale metrology and annotations for deep learning automation on electron microscopy images","authors":"I. Sanou, J. Baderot, Y. Benezeth, S. Bricq, F. Marzani, S. Martínez, J. Foucher","doi":"10.1117/12.2690493","DOIUrl":"https://doi.org/10.1117/12.2690493","url":null,"abstract":"For semiconductor applications, billions of objects are manufactured for a single device such as a central processing unit (CPU), storage drive, or graphical processing unit (GPU). To obtain functional devices, each element of the device has to follow precise dimensional and physical specifications at the nanoscale. Generally, the pipeline consists to annotate an object in an image and then take the measurements of the object. Manually annotating images is extremely time-consuming. In this paper, we propose a robust and fast semi-automatic method to annotate an object in a microscopy image. The approach is a deep learning contour-based method able first to detect the object and after finding the contour thanks to a constraint loss function. This constraint follows the physical meaning of electron microscopy images. It improves the quality of boundary detail of the vertices of each object by matching the predicted vertices and most likely the contour. The loss is computed during training for each object using a proximal way of our dataset. The approach was tested on 3 different types of datasets. The experiments showed that our approaches can achieve state-of-the-art performance on several microscopy images dataset.","PeriodicalId":295011,"journal":{"name":"International Conference on Quality Control by Artificial Vision","volume":"12749 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":"131309662","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

Registration of thermal 3D models over time using low cost and portable devices 随着时间的推移，使用低成本和便携式设备的热3D模型注册

International Conference on Quality Control by Artificial Vision Pub Date : 2023-07-28 DOI: 10.1117/12.3000628

Evelyn Gutierrez, B. Castañeda, S. Treuillet

引用次数: 0

Object detection model-based quality inspection using a deep CNN 基于物体检测模型的深度CNN质量检测

International Conference on Quality Control by Artificial Vision Pub Date : 2023-07-28 DOI: 10.1117/12.2689921

Mohamed Chetoui, M. Akhloufi

{"title":"Object detection model-based quality inspection using a deep CNN","authors":"Mohamed Chetoui, M. Akhloufi","doi":"10.1117/12.2689921","DOIUrl":"https://doi.org/10.1117/12.2689921","url":null,"abstract":"In the competitive world of the metal industry where companies have to offer quality products, quality control is crucial. However, it takes a considerable amount of time, especially if it is a manual process. Automatic Fault Detection (AFD) system reduces a lot of work for the companies, saves time, money and improves use of available resources. Deep learning can be efficiently used to develop such a AFD system. In this article, we present the development of deep learning (DL) algorithms for quality control. We trained State-of the-art DL (YOLO v8n, YOLO v8s, YOLO v8m, YOLO v8l and YOLO v8x) for a quality control task using a manually annotated dataset of 3 classes (neck scratch, scratch and bent) for 2 objects (Screw and Metal Nut). The results show very interesting scores for YOLO v8s with an mAP@0.50 of 90.60%, a precision of 100% and a recall of 94.0% for the 3 classes on average. We also compared the performance of these models with a popular DL model detector called Faster-RCNN x101 in order to confirm the performance of the developed models. The qualitative results show good detection of defects with different sizes (small, medium and large). Our proposition gives very interesting results to deploy an AFD system for metal industries.","PeriodicalId":295011,"journal":{"name":"International Conference on Quality Control by Artificial Vision","volume":"33 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":"114978538","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

Person segmentation and identification across multiple wearable cameras 跨多个可穿戴摄像头的人物分割和识别

International Conference on Quality Control by Artificial Vision Pub Date : 2023-07-28 DOI: 10.1117/12.2692433

Noriko Takemura, Haruya Sakashita, Shizuka Shirai, Mehrasa Alizadeh, Hajime Nagahara

引用次数: 0

Co-design of an active depth from defocus camera for surface inspection 参与设计一种用于表面检测的主动离焦深度相机

International Conference on Quality Control by Artificial Vision Pub Date : 2023-07-28 DOI: 10.1117/12.2692630

B. Buat, P. Trouvé-Peloux, F. Champagnat, T. Simon

引用次数: 0

Visual inspection of complex mechanical assemblies based on Siamese networks for 3D point clouds 基于三维点云Siamese网络的复杂机械组件视觉检测

International Conference on Quality Control by Artificial Vision Pub Date : 2023-07-28 DOI: 10.1117/12.2692751

Velibor Došljak, Igor Jovančević, J. Orteu

引用次数: 0

OLF: RGB-D adaptive late fusion for robust 6D pose estimation 基于RGB-D自适应后期融合的稳健6D姿态估计

International Conference on Quality Control by Artificial Vision Pub Date : 2023-07-28 DOI: 10.1117/12.2690943

Théo Petitjean, Zongwei Wu, C. Demonceaux, O. Laligant

{"title":"OLF: RGB-D adaptive late fusion for robust 6D pose estimation","authors":"Théo Petitjean, Zongwei Wu, C. Demonceaux, O. Laligant","doi":"10.1117/12.2690943","DOIUrl":"https://doi.org/10.1117/12.2690943","url":null,"abstract":"RGB-D 6D pose estimation has recently gained significant research attention due to the complementary information provided by depth data. However, in real-world scenarios, especially in industrial applications, the depth and color images are often more noisy1 . 2 Existing methods typically employ fusion designs that equally average RGB and depth features, which may not be optimal. In this paper, we propose a novel fusion design that adaptively merges RGB-D cues. Our approach involves assigning two learnable weight α1 and α2 to adjust the RGB and depth contributions with respect to the network depth. This enables us to improve the robustness against low-quality depth input in a simple yet effective manner. We conducted extensive experiments on the 6D pose estimation benchmark and demonstrated the effectiveness of our method. We evaluated our network in conjunction with DenseFusion on two datasets (LineMod3 and YCB4) using similar noise scenarios to verify the usefulness of reinforcing the fusion with the α1 and α2 parameters. Our experiments show that our method outperforms existing methods, particularly in low-quality depth input scenarios. We plan to make our source code publicly available for future research.","PeriodicalId":295011,"journal":{"name":"International Conference on Quality Control by Artificial Vision","volume":"12749 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":"129138824","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

Separation of compound actions with wrist and finger based on EMG 基于肌电图的腕指复合动作分离

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2585334

Eisuke Yamamoto, Momoyo Ito, S. Ito, M. Fukumi

引用次数: 0

Identify solar panel defects by using differences between solar panels 利用太阳能电池板之间的差异来识别太阳能电池板缺陷

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2586911

J. Deng, T. Minematsu, A. Shimada, R. Taniguchi

引用次数: 1

Latent space visualization of half face and full face by generative model 基于生成模型的半脸和全脸潜在空间可视化

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2588980

Zou Min, T. Akashi

{"title":"Latent space visualization of half face and full face by generative model","authors":"Zou Min, T. Akashi","doi":"10.1117/12.2588980","DOIUrl":"https://doi.org/10.1117/12.2588980","url":null,"abstract":"Generally, most face detection and recognition tasks are based on the training of intact facial images and their corresponding labels. The training image is supposed to contain as much facial area as possible, and sometimes expanding the training image area to the upper body may also enhance the learning ability. However, we noticed that both the three-dimensional structure and two-dimensional appearance from the frontal view of human faces are bilaterally symmetrical. Few research makes use of this characteristics to simplify the learning process. We have proposed a flipping strategy to apply the facial symmetrical characteristic to transfer learning and proved training with half faces can also achieve equivalent performance in face recognition for a small group of individuals. This paper extend the transfer learning of cropped half face images for face recognition rather than flipping the half face. The facial symmetrical characteristics is utilized to improve face recognition through transfer learning of only a half of the common human face image. We also investigate and explain the reason why the half face area is enough to accurately classify small groups of individuals. A variational autoencoder network is utilized to impose the probability distribution on the facial latent space. Finally, the dimensions of the facial latent space are reduced to visualize the distributed perceptual manifold for face identity.","PeriodicalId":295011,"journal":{"name":"International Conference on Quality Control by Artificial Vision","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121838068","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