International Conference on Quality Control by Artificial Vision最新文献_第7页

R-CNN based automated visual inspection system for engine parts quality assessment 基于R-CNN的发动机部件质量评估自动目视检测系统

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2586575

A. Léger, G. le Goic, E. Fauvet, D. Fofi, Rémi Kornalewski

引用次数: 2

Automatic recognition of parasitic bee species using wing vein shape 利用翅脉形状自动识别寄生蜂种类

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2589061

Yusuke Misaki, K. Terada

引用次数: 0

An intelligent traffic control systems using on-road cameras 一种采用道路摄像头的智能交通控制系统

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2589192

Md Mehedi Hassan, S. Karungaru, K. Terada

{"title":"An intelligent traffic control systems using on-road cameras","authors":"Md Mehedi Hassan, S. Karungaru, K. Terada","doi":"10.1117/12.2589192","DOIUrl":"https://doi.org/10.1117/12.2589192","url":null,"abstract":"The main reasons behind the traffic jam and accidents are illegal/double parking, over-speeding, violating signal lights, construction, wrong-way driving, reckless driving, unsafe lane changing, etc. To determines the problems and the solutions, this study proposes a two-step approach. One is data collection and the other is Optimization. In the data collection part, traffic information is obtained from various traffic information units through cameras installed in traffic signals and roads. By analyzing the collected data, the systems can take the next step in the optimization part. In the collected data, it is required to detect vehicles, pedestrians, and lanes. Yolov3 method was used for vehicles and pedestrians’ detection. For lane detection, the Hough transform was used. The main goal of the research is not detecting objects but to determine the intelligent systems which can combine all the collected data and give the optimum solutions to control the traffic signals depending on the situations. The study found that sometimes the vehicles are unnecessarily waiting for the signals. If the unnecessary time could be saved through signals then it would reduce time consumption, oil consumption, and mental impatience. The result would give us opportunities to reduce accidents, pollution, money, and time. Moreover, the systems can measure the speed which helps find out rule violating vehicles. This paper also proposed a method for shortest path calculation. In addition, automatic penalty execution can be carried out through the collected data. For that number plate recognition is included in future works.","PeriodicalId":295011,"journal":{"name":"International Conference on Quality Control by Artificial Vision","volume":"31 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":"132066521","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

Quality assessment of dynamic virtual relighting from RTI data: application to the inspection of engineering surfaces 基于RTI数据的动态虚拟重照质量评估:在工程表面检测中的应用

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2589178

A. Zendagui, G. L. Le Goïc, H. Chatoux, Jean-Baptiste Thomas, Y. Castro, M. Nurit, A. Mansouri

引用次数: 4

Tiny range image sensors using multiple laser lights for short distance measurement 使用多个激光光源进行短距离测量的微型范围图像传感器

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2589196

Tomoaki Fukuda, Yonghoon Ji, K. Umeda

{"title":"Tiny range image sensors using multiple laser lights for short distance measurement","authors":"Tomoaki Fukuda, Yonghoon Ji, K. Umeda","doi":"10.1117/12.2589196","DOIUrl":"https://doi.org/10.1117/12.2589196","url":null,"abstract":"This paper presents very compact range image sensors for short distance measurement, which is suitable for robot hands, etc. Robot manipulation such as grasping is one of the applications that require a range image sensor to obtain threedimensional (3D) information of the target object. For such applications, it is necessary to avoid the occlusion by a robot manipulator or a robot hand while measurement, and it is effective to attach a sensor to the robot hand for the avoidance. For this aim, a range sensor that is small enough and can measure at the short distance is required. Two sensors are constructed in this paper: one uses a multi-slit laser projector and the other uses a multi-spot laser projector. A small laser projector and a small camera is combined and range images are obtained in real time using the principle of active stereo. Appropriate methods to obtain range image are proposed for both sensors, and especially for the one with a multislit laser projector, a method to use both disparity and the intensity of laser light image is presented. The effectiveness of the proposed sensors is verified through short-range object measurement experiments.","PeriodicalId":295011,"journal":{"name":"International Conference on Quality Control by Artificial Vision","volume":"43 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":"114973245","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

Personal authentication and recognition of aerial input Hiragana using deep neural network 基于深度神经网络的航拍输入平假名的个人认证与识别

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2585333

H. Mimura, Momoyo Ito, S. Ito, M. Fukumi

引用次数: 0

Evaluation of shot peening machined surface by image processing 喷丸强化加工表面的图像评价

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2585193

Tsuyoshi Shimizu, Yasutake Haramiishi, Y. A. Rahim, Syamir Alihan, Yuji Kobayashi, A. Matsui, Shinji Kotani, H. Watanabe

引用次数: 0

Training PointNet for human point cloud segmentation with 3D meshes 训练PointNet的人类点云分割与3D网格

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2589075

Takuma Ueshima, Katsuya Hotta, Shogo Tokai, Chao Zhang

引用次数: 2

Scan quality estimation for industrial computed tomography using convolutional neural networks 基于卷积神经网络的工业计算机断层扫描质量估计

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2588990

Manuel Kaufmann, V. Volland, Yifei Chen, I. Effenberger, C. Veyhl

{"title":"Scan quality estimation for industrial computed tomography using convolutional neural networks","authors":"Manuel Kaufmann, V. Volland, Yifei Chen, I. Effenberger, C. Veyhl","doi":"10.1117/12.2588990","DOIUrl":"https://doi.org/10.1117/12.2588990","url":null,"abstract":"Artefacts in industrial Computed Tomography (CT) compromise the image quality of a CT scan and deteriorate evaluations such as inspections for material defects or dimensional measurements. Due to a large variety of scanning objects made of different materials and of various part sizes, artefacts appear in various manifestations in the reconstructed image. Existing analytical approaches allow quantifying the CT scan quality, but still a lack of generalizability exists. Thus, assessing the scan quality is complex and error-prone, as an inappropriate set of analytical quality metrics might be considered for a certain scan setup. In our work, a scan quality estimation based on a Convolutional Neural Network (CNN) is proposed. In order to train the network, projection images of various scans are used. The reconstructed scans are labeled in a pairwise comparison by an experienced user regarding their image quality. A scalar quality value is assigned to every projection image to assess the quality. The network is deployed to perform regression for the quality value. The network is trained on multiple objects that cover the range of objects which can be sufficiently acquired with the used CT scanner. In order to enrich the features from scans of different qualities, each object is captured with various scanning parameters. Our work showed a test accuracy of approximately 80 % on prior unseen data and of up to 95 % on trained objects. In order to comprehend the black box approach incorporated by the trained CNN, visualizations of feature maps are analyzed, as regions in the projection images relevant for the quality estimation are highlighted.","PeriodicalId":295011,"journal":{"name":"International Conference on Quality Control by Artificial Vision","volume":"9 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":"131142191","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

Supporting sports instruction with comparative display of forms 以形式对比展示辅助体育教学

International Conference on Quality Control by Artificial Vision Pub Date : 2021-07-16 DOI: 10.1117/12.2589101

Sota Akiyama, Nobuyuki Umezu

{"title":"Supporting sports instruction with comparative display of forms","authors":"Sota Akiyama, Nobuyuki Umezu","doi":"10.1117/12.2589101","DOIUrl":"https://doi.org/10.1117/12.2589101","url":null,"abstract":"In this research, we propose a method to support sports instruction by displaying visualized comparison between a model motion and that of the player. Human pose estimation with the OpenPose framework based on deep learning technologies is first applied to both model and user motions. The motion difference between the model and the user is visualized with their trajectories superimposed on those two motions. To help users improve their motions and forms, our system displays where the specific body part, for example, the elbow of the throwing arm, should be located at a specific time, computed from the location and timing in the model motion. Our system also presents changes of velocity and position of body parts over time with line graphs for supposing users better understand that motion. Our current implementation is designed for pitching motions, and several other motions is included in our future work. We conducted user experiments where four participants used our system to improve their pitching forms. After recording pitching motion of these four participants, they were divided into two groups. First two participants were given the visualizations of our system while other two were presented only a pair of motions from a model and their own. The participants then performed their improved pitching motions again in front of our system to evaluate which of these two groups showed more refinements. The experimental results have shown that their forms are improved in both groups, and the usability of the proposed system has not successfully tested. Future work includes conducting larger-scale experiments, automatic selection of model motions for each user, as well as extending our method to other motions in different sports.","PeriodicalId":295011,"journal":{"name":"International Conference on Quality Control by Artificial Vision","volume":"33 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":"115260929","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