scholarly journals Research on CNN-based intelligent recognition method for negative images of weld defects

2021 ◽  
Vol 2093 (1) ◽  
pp. 012020
Author(s):  
Jiawei HUANG ◽  
Caixia BI ◽  
Jiayue LIU ◽  
Shaohua DONG
Keyword(s):  
Recognition Accuracy ◽  
Recognition Algorithm ◽  
Recognition Method ◽  
Negative Image ◽  
Weld Defects ◽  
Negative Images ◽  
High Recognition Accuracy ◽  
Defect Recognition ◽  
Image Defect ◽  
Intelligent Recognition

Abstract The existing technology of automatic classification and recognition of welding negative images by computer is difficult to achieve a multiple classification defect recognition while maintaining a high recognition accuracy, and the developed automatic recognition model of negative image defect cannot meet the actual needs of the field. Therefore, the convolutional neural network (CNN)-based intelligent recognition algorithm for negative image of weld defects is proposed, and a B/S (Browser/Server) architecture of weld defect feature image database combined with CNN is established subsequently, which converted from the existing CNN by the migration learning method. It makes full use of the negative big data and simplifies the algorithm development process, so that the recognition algorithm has a better generalization ability and the training algorithm accuracy of 97.18% achieved after training. The results of the comparison experiments with traditional recognition algorithms show that the CNN-based intelligent recognition algorithm for defective weld negatives has an accuracy of 92.31% for dichotomous defects, which is significantly better than the traditional recognition algorithm, the established recognition algorithm effectively improving the recognition accuracy and achieving multi-category defect recognition. At the same time, the CNN-based defect recognition method was established by combining the image segmentation algorithm and the defect intelligent recognition algorithm, which was applied to the actual negative images in the field with good results, further verifying the feasibility of CNN-based intelligent recognition algorithm in the field of defect recognition of welding negative images.

scholarly journals Design and Image Research of Tennis Line Examination Based on Machine Vision Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Liu Yan ◽  
Sun Xin
Keyword(s):  
Machine Vision ◽  
Camera Calibration ◽  
Recognition Accuracy ◽  
Calibration Method ◽  
Recognition Method ◽  
Systematic Analysis ◽  
High Recognition Accuracy ◽  
Intelligent Recognition ◽  
Region Growth ◽  
Design Experiments

In view of the intelligent demand of tennis line examination, this paper performs a systematic analysis on the intelligent recognition of tennis line examination. Then, a tennis line recognition method based on machine vision is proposed. In this paper, the color region of the image recognition region is divided based on the region growth, and the rough estimation of the court boundary is realized. In order to achieve the effect of camera calibration, a fast camera calibration method which can be used for a variety of court types is proposed. On the basis of camera calibration, a tennis line examination and segmentation system based on machine vision analysis is constructed, and the experimental results are verified by design experiments. The results show that the machine vision analysis-based intelligent segmentation system of tennis line examination has high recognition accuracy and can meet the actual needs of tennis line examination.

scholarly journals Simulation and Recognition of Concrete Lining Infiltration Degree via an Indoor Experiment

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Dongsheng Wang ◽  
Jun Feng ◽  
Xinpeng Zhao ◽  
Yeping Bai ◽  
Yujie Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cement Mortar ◽  
Recognition Accuracy ◽  
Image Data ◽  
Concrete Lining ◽  
Learning Method ◽  
Recognition Method ◽  
Data Set ◽  
Recognition Model ◽  
High Recognition Accuracy

It is difficult to form a method for recognizing the degree of infiltration of a tunnel lining. To solve this problem, we propose a recognition method by using a deep convolutional neural network. We carry out laboratory tests, prepare cement mortar specimens with different saturation levels, simulate different degrees of infiltration of tunnel concrete linings, and establish an infrared thermal image data set with different degrees of infiltration. Then, based on a deep learning method, the data set is trained using the Faster R-CNN+ResNet101 network, and a recognition model is established. The experiments show that the recognition model established by the deep learning method can be used to select cement mortar specimens with different degrees of infiltration by using an accurately minimized rectangular outer frame. This model shows that the classification recognition model for tunnel concrete lining infiltration established by the indoor experimental method has high recognition accuracy.

scholarly journals Aerobics Action Recognition Algorithm Based on Three-Dimensional Convolutional Neural Network and Multilabel Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Qian Wang ◽  
Mingzhe Wang
Keyword(s):  
Three Dimensional ◽  
Recognition Algorithm ◽  
Recognition Method ◽  
Simulation Experiments ◽  
Multilabel Classification ◽  
Convolutional Networks ◽  
Human Movements ◽  
Video Recognition ◽  
Intelligent Recognition ◽  
Time Information

In the context of modern people increasingly paying attention to health and promoting aerobics, the amount of data and audiences of aerobics videos has grown rapidly, and its potential application value has attracted widespread attention from scientific research and industry perspectives. This article has integrated computer vision and deep learning related knowledge to realize the intelligent recognition and representation of specific human movements in aerobics video sequences. The study proposes an automatic recognition method for floor exercise videos based on three-dimensional convolutional networks and multilabel classification. Since two-dimensional convolutional neural networks (CNNs) lose time information when extracting features, so to overcome this, the proposed research uses three-dimensional convolutional networks to perform video recognition. The feature is taken in time and space, and the extracted features are subjected to multiple binary classifications to achieve the goal of multilabel classification. Various comparison and simulation experiments are conducted for the proposed research, and the experimental results prove the effectiveness and superiority of the approach.

scholarly journals Recognition of Crop Diseases Based on Depthwise Separable Convolution in Edge Computing

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4091
Author(s):  
Musong Gu ◽  
Kuan-Ching Li ◽  
Zhongwen Li ◽  
Qiyi Han ◽  
Wenjie Fan
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Recognition Accuracy ◽  
Recognition Algorithm ◽  
Edge Computing ◽  
Recognition Model ◽  
Fast Recognition ◽  
Proposed Model ◽  
Crop Diseases ◽  
High Recognition Accuracy

The original pattern recognition and classification of crop diseases needs to collect a large amount of data in the field and send them next to a computer server through the network for recognition and classification. This method usually takes a long time, is expensive, and is difficult to carry out for timely monitoring of crop diseases, causing delays to diagnosis and treatment. With the emergence of edge computing, one can attempt to deploy the pattern recognition algorithm to the farmland environment and monitor the growth of crops promptly. However, due to the limited resources of the edge device, the original deep recognition model is challenging to apply. Due to this, in this article, a recognition model based on a depthwise separable convolutional neural network (DSCNN) is proposed, which operation particularities include a significant reduction in the number of parameters and the amount of computation, making the proposed design well suited for the edge. To show its effectiveness, simulation results are compared with the main convolution neural network (CNN) models LeNet and Visual Geometry Group Network (VGGNet) and show that, based on high recognition accuracy, the recognition time of the proposed model is reduced by 80.9% and 94.4%, respectively. Given its fast recognition speed and high recognition accuracy, the model is suitable for the real-time monitoring and recognition of crop diseases by provisioning remote embedded equipment and deploying the proposed model using edge computing.

scholarly journals An Novel Image Recognition method based on Three-way Decision

2018 ◽  
Vol 176 ◽  
pp. 01035
Author(s):  
Jin Dai ◽  
Shuai Shao ◽  
Zu Wang ◽  
Xianjing Zhao
Keyword(s):  
Image Recognition ◽  
Iterative Process ◽  
Recognition Accuracy ◽  
Recognition Algorithm ◽  
Positive Sample ◽  
Human Cognition ◽  
Recognition Method ◽  
Decision Region ◽  
Level Information ◽  
Delay Decision

The traditional recognition method takes the low-level information of the image as the foundation. The image recognition center of gravity is biased towards the typical features, and achieves the effect of recognition by region-dependent segmentation. Because the general image segmentation is a regular rectangle, easily lead to the same target is divided into different sub-blocks, ignoring the image of the fuzzy part, so the image recognition is not complete. An image recognition algorithm based on threeway decision is proposed. It takes full advantage of effective information in the image, improving the image recognition accuracy. First, this method divided the image into three regions: positive region, negative region and delay decision region. Second, an iterative process is performed on the region of the delay decision. Final, image recognition is performed on the positive sample region. Based on the basic theory of the three-way decision, the more obvious the decision result is, the more iterations are, and the information is added to the classifier until the blurred part of image cannot be divided. Finally, to achieve the realize effective image recognition. This method simulates the process of human cognition effectively, and makes the utilization of the effective information reach the maximum in the recognition process. The results of the experimental analysis showed that the method is more concise and efficient, and the recognition accuracy is more accurate.

Discriminative bit selection hashing in RGB-D based object recognition for robot vision

2019 ◽  
Vol 39 (1) ◽  
pp. 17-25 ◽  
Author(s):  
Lin Feng ◽  
Yang Liu ◽  
Zan Li ◽  
Meng Zhang ◽  
Feilong Wang ◽  
...  
Keyword(s):  
Object Recognition ◽  
Recognition Accuracy ◽  
Robot Vision ◽  
Binary Representation ◽  
Recognition Method ◽  
Content Type ◽  
Selection Pattern ◽  
Approximate Nearest Neighbors ◽  
Label Information ◽  
High Recognition Accuracy

PurposeThe purpose of this paper is to promote the efficiency of RGB-depth (RGB-D)-based object recognition in robot vision and find discriminative binary representations for RGB-D based objects.Design/methodology/approachTo promote the efficiency of RGB-D-based object recognition in robot vision, this paper applies hashing methods to RGB-D-based object recognition by utilizing the approximate nearest neighbors (ANN) to vote for the final result. To improve the object recognition accuracy in robot vision, an “Encoding+Selection” binary representation generation pattern is proposed. “Encoding+Selection” pattern can generate more discriminative binary representations for RGB-D-based objects. Moreover, label information is utilized to enhance the discrimination of each bit, which guarantees that the most discriminative bits can be selected.FindingsThe experiment results validate that the ANN-based voting recognition method is more efficient and effective compared to traditional recognition method in RGB-D-based object recognition for robot vision. Moreover, the effectiveness of the proposed bit selection method is also validated to be effective.Originality/valueHashing learning is applied to RGB-D-based object recognition, which significantly promotes the recognition efficiency for robot vision while maintaining high recognition accuracy. Besides, the “Encoding+Selection” pattern is utilized in the process of binary encoding, which effectively enhances the discrimination of binary representations for objects.

scholarly journals Weld Defect Detection and Image Defect Recognition Using Deep Learning Technology

2021 ◽  
Author(s):  
Yu Cheng ◽  
HongGui Deng ◽  
YuXin Feng ◽  
JunJiang Xiang
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Defect Detection ◽  
Learning Algorithm ◽  
Image Features ◽  
Economic Losses ◽  
Weld Defects ◽  
Weld Defect ◽  
Defect Recognition ◽  
Image Defect

Abstract Welding defects not only bring several economic losses to enterprises and individuals but also threatens peoples lives. We propose a deep learning model, where the data-trained deep learning algorithm is employed to detect the weld defects, and the Convolutional Neural Networks (CNNs) are utilized to recognize the image features. The Transfer Learning (TL) is adopted to reduce the training time via simple adjustments and hyperparameter regulations. The designed deep learning-based model is compared with other classic models to prove its effectiveness in weld defect detection and image recognition further. The results show this model can accurately identify weld defects and eliminates the complexity of manually extracting features, reaching a recognition accuracy of 92.54%. Hence, the reliability and automation of detection and recognition is improved signifificantly. Actual application also verififies the effectiveness of TL in weld defect detection and image defect recognition. Therefore, our research results can provide theoretical and practical references for effificient automatic detection of steel plates, cost reduction, and the high-quality development of iron and steel enterprises.Index Terms - convolutional neural network, deep learning, image detect recognition, transfer learning, weld defect detection

Implementation of Embedded Unspecific Continuous English Speech Recognition Based on HMM

2021 ◽  
Vol 14 ◽  
Author(s):  
Xiaoli Lu ◽  
Mohd Asif Shah
Keyword(s):  
Speech Recognition ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Vital Role ◽  
Recognition Algorithm ◽  
Theoretical Research ◽  
Fast Speed ◽  
Conversational Interfaces ◽  
Recognition Function ◽  
High Recognition Accuracy

Background: Human-computer interaction plays a vital role through Natural Language Conversational Interfaces to improve the usage of computers. Speech recognition technology allows the machine to understand human language. A speech recognition algorithm is used to achieve this function. Methodology: This paper is mainly based on the fundamental theoretical research of speech signals, establishes the HMM model, uses speech collection, recognition, and other methods, simulates on MATLAB, and integrates the recognition system ported to ARM for debugging and running to realize the embedded speech recognition function based on HMM under the ARM platform. Conclusion: The conclusion shows that the HMM-based embedded unspecific continuous English speech recognition system has high recognition accuracy and fast speed.

Computer Extraction and Recognition Method Research on Weld Defect

2013 ◽  
Vol 739 ◽  
pp. 210-213
Author(s):  
Peng Lin Zhang ◽  
Zheng Bin Wu ◽  
Xian Ming Niu ◽  
Zhi Qiang Zhao
Keyword(s):  
Extraction Method ◽  
Recognition Rate ◽  
Recognition Method ◽  
Image Preprocessing ◽  
Weld Defect ◽  
Feature Parameter ◽  
Defect Recognition ◽  
Image Defect ◽  
High Lead ◽  
Defect Extraction

This paper carry out a kind of defect extraction method .Aiming at the weld image defect extraction accuracy is not high and defect feature selection is undeserved, thus affecting defect recognition rate is not high lead to falsely accused of miscarriage of justice on this condition.Based on image preprocessing to remove noise and strengthen the image, and then the image analysis so as to extract defect finally take defect marking defect feature parameter selection, in order to accurately identify defect.

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