Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824686

Wanbo Luo

{"title":"Recognition of surface defects of aluminum profiles based on convolutional neural network","authors":"Wanbo Luo","doi":"10.1109/cvidliccea56201.2022.9824686","DOIUrl":"https://doi.org/10.1109/cvidliccea56201.2022.9824686","url":null,"abstract":"Many manufacturers will strictly control the quality of products, especially the surface quality of products. Under the same conditions, the better the surface quality of the product, the more competitive it is. Many aluminum profile benchmarking companies have pain points with flaws on the surface of their products. Due to the work mistakes of the workers in the production workshop, unqualified aluminum materials need to be eliminated in the product production control, and the traditional method is to rely on the assembly line workers to check one by one. As the company’s production automation continues to increase, the shortcomings of manual inspection methods have become increasingly prominent. Aiming at the common types of surface defects in the company’s aluminum profile production process, this paper introduces the deep learning method into the identification of aluminum profile surface defects and uses convolutional neural network to identify the surface defects of aluminum profiles. The advantages and disadvantages of different aluminum profile surface defect recognition models such as AlexNet, VGG19 and Inception V4 are analyzed. Finally, according to the recognition effect of the aluminum profile data set, the recognition model of aluminum profile surface defects based on Inception V4 is selected as the optimal model.","PeriodicalId":23649,"journal":{"name":"Vision","volume":"56 8 1","pages":"5-8"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89687454","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: 1

Ceramic ring defect detection based on improved YOLOv5 基于改进YOLOv5的陶瓷环缺陷检测

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824099

Shengqi Guan, Xu Wang, Jingguo Wang, Zijiang Yu, Xizhi Wang, Chao Zhang, Tong Liu, Dongdong Liu, Junqiang Wang, Libo Zhang

引用次数: 3

A multi-sensor information fusion monitoring system for photovoltaic power generation 光伏发电多传感器信息融合监测系统

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824758

Xiao Wang, Bo Zhao, Shengxian Cao, Siyuan Fan

引用次数: 0

Light field structured light projection data generation with Blender 使用Blender生成光场结构光投影数据

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824921

Xinjun Zhu, Zhizhi Zhang, Linpeng Hou, Limei Song, Hongyi Wang

{"title":"Light field structured light projection data generation with Blender","authors":"Xinjun Zhu, Zhizhi Zhang, Linpeng Hou, Limei Song, Hongyi Wang","doi":"10.1109/cvidliccea56201.2022.9824921","DOIUrl":"https://doi.org/10.1109/cvidliccea56201.2022.9824921","url":null,"abstract":"Light field structured light 3D measurement has gained popularity by merging the advantages of light field and structured light methods. Generating light field structured light dataset is necessary for studying light field 3D reconstruction algorithms, but it is time-consuming and expensive in a real sense, especially for ground truth data. This paper proposes a method to generate light field structured light projection data with Blender simulation. The proposed method allows for the modification of camera and projector settings and parameters, as well as rotating objects. The dataset generated by this method contains 107730 light field structured light images. The label data (ground truth data) including depth and disparity by the 9×9 light field camera array are provided for the performance evaluation of 3D reconstruction algorithms. To the best of our knowledge, it is the first public dataset in the light field structured light projection environment. Diverse 3D reconstruction methods, including deep learning methods, are used to evaluate the proposed data generation method and dataset. The dataset is available at https://github.com/sabaizzz/Light-field-structured-light-dataset.","PeriodicalId":23649,"journal":{"name":"Vision","volume":"14 1","pages":"1249-1253"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78143540","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: 1

A Location Analysis for Dynamic Verification 动态验证的位置分析

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824849

Jingyao Nie, Shuqin Geng, Xiaohong Peng, Wenhua Cao, Pengkun Li, Xuefeng Li

引用次数: 0

Indoor Monocular Image Depth Estimation Based on Semantic Information of Tree-shaped ASPP Structure 基于树形ASPP结构语义信息的室内单眼图像深度估计

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9825336

Zhisong Bai, Chao Zhang, Cheng Han, Linke Zhang

{"title":"Indoor Monocular Image Depth Estimation Based on Semantic Information of Tree-shaped ASPP Structure","authors":"Zhisong Bai, Chao Zhang, Cheng Han, Linke Zhang","doi":"10.1109/cvidliccea56201.2022.9825336","DOIUrl":"https://doi.org/10.1109/cvidliccea56201.2022.9825336","url":null,"abstract":"ASPP (Atrous Spatial Pooling Pyramid) has the advantage that it can expand the receptive field and extract multiscale features without changing the image resolution. We introduce it into the depth estimation task to improve the problems of inaccurate depth estimation, blurred edges, and loss of depth information details on the current unsupervised depth estimation methods for indoor monocular images. However, the ASPP module does not consider the relationship between different pixel features, resulting in inaccurate extraction of scene features in the depth estimation task. Therefore, we propose a Tree-shaped ASPP structure for this drawback, combined with the SC-SfMLearner network using the NYUv2 dataset, adding the spatial semantic information pool formed by the ASPP tree structure between the encoder and decoder structures of the depth estimation network, which can not only expand the receptive field without losing resolution but also capture and fuse multi-scale context information, so that different pixels establish connections between features. The results show that, compared with the original method, the improved method has stronger network feature extraction ability, clearer contours of each target in the scene, more distinct layers, and more accurate depth estimation results.","PeriodicalId":23649,"journal":{"name":"Vision","volume":"56 1","pages":"330-333"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84558711","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

Research on reliability analysis method of wireless communication hardware system 无线通信硬件系统可靠性分析方法研究

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824420

J. Sun, Neng He, Jiawen Zhang, Huameng Gao, Fenling Qi, Hongjiang Yang

引用次数: 3

COVID-19 Classification with CT Scan and Advanced Deep Learning Technologies 基于CT扫描和先进深度学习技术的COVID-19分类

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824858

Zi-Hua Li

引用次数: 1

A Predict Method of Measuring Equipment Operation Performance Based on Improved Local Weighted Partial Least Squares 基于改进局部加权偏最小二乘的设备运行性能预测方法

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824242

Mingji Li, Ning Cao, Hao Lu, Fan Gao

{"title":"A Predict Method of Measuring Equipment Operation Performance Based on Improved Local Weighted Partial Least Squares","authors":"Mingji Li, Ning Cao, Hao Lu, Fan Gao","doi":"10.1109/cvidliccea56201.2022.9824242","DOIUrl":"https://doi.org/10.1109/cvidliccea56201.2022.9824242","url":null,"abstract":"In view of the fact that the operation data of metering equipment in the power system has strong nonlinearity and immediacy, traditional methods cannot effectively model, predict and analyze. This paper proposes an improved local weighted partial least squares algorithm (K-MLWPLS) based on K-means clustering. The method first uses the K-means clustering algorithm to divide the training set into k sub-training sets; then uses the locally weighted partial least squares algorithm combined with the Two-scale similarity measure to model the sub-training sets, and uses grid search and Cross-validation adjusts the model parameters to obtain the optimized k sub-models; then for the test set samples, the sub-models weighted based on the centroid neighbour’s radius are integrated to calculate the final predicted value corresponding to the test set samples. The algorithm is applied to the prediction analysis of the operation data of the metering equipment. The experimental results show that the K-MLWPLS algorithm significantly improves the prediction accuracy of the model compared with the traditional modeling algorithm.","PeriodicalId":23649,"journal":{"name":"Vision","volume":"12 1","pages":"41-45"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84921186","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

Human fall detection based on improved particle swarm optimization algorithm and neural network 基于改进粒子群优化算法和神经网络的人体跌倒检测

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9823997

Chaowei Zhou, J. Xiao, Aimin Xiong, Caifeng Zhang

引用次数: 1

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