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 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 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 A Camera Calibration Method with Large Oblique Angle Machine Vision

2018 ◽  
Vol 435 ◽  
pp. 012040
Author(s):  
Meng Chen ◽  
Yue Lei He ◽  
Pei Yi Gong ◽  
Zai Wei Li ◽  
Hong Yao Lu
Keyword(s):  
Machine Vision ◽  
Camera Calibration ◽  
Calibration Method ◽  
Oblique Angle

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 Intelligent Recognition of Insulator Contamination Grade Based on the Deep Learning of Ultraviolet Discharge Image Information

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5221
Author(s):  
Da Zhang ◽  
Shuailin Chen
Keyword(s):  
Deep Learning ◽  
Recognition Accuracy ◽  
Original Data ◽  
Recognition Method ◽  
Spot Area ◽  
Sparse Autoencoder ◽  
Contamination Flashover ◽  
Intelligent Recognition ◽  
Sparse Features ◽  
Accuracy Rates

In order to achieve the noncontact detection of the contamination grade of insulators and to provide guidance for preventing the contamination flashover of insulators based on the pollution state, we propose a contamination grade recognition method based on the deep learning of ultraviolet discharge images using a sparse autoencoder (SAE) and a deep belief network (DBN). Under different humidity conditions, we filmed and preprocessed the ultraviolet discharge images of insulators at different contamination grades and we obtained the ultraviolet spot area sequence as original data for contamination grade recognition. A double-layer sparse autoencoder was used to extract sparse features that could characterize different contamination grades from the ultraviolet spot area sequence. Using the extracted features, a DBN composed of three layers of restricted Boltzmann machine was trained to provide contamination grade recognition. To verify the effectiveness of the method proposed in this paper, high-voltage experiments were performed on contaminated insulators at relative humidity levels of 80%, 85%, and 90%, and ultraviolet images were recorded. The proposed SAE–DBN method was used to identify the ultraviolet images of the insulators with different contamination grades. The recognition accuracy rates at the three humidity levels were 91.25%, 93.125%, and 92.5%. The experimental results showed that this method could accurately recognize the contamination grade of the insulator and provide guidance for the prevention of contamination flashover based on the pollution severity.

Machete

2021 ◽  
Vol 28 (1) ◽  
pp. 1-46
Author(s):  
Eugene M. Taranta II ◽  
Corey R. Pittman ◽  
Mehran Maghoumi ◽  
Mykola Maslych ◽  
Yasmine M. Moolenaar ◽  
...  
Keyword(s):  
Video Game ◽  
Recognition Accuracy ◽  
Training Sample ◽  
Low Latency ◽  
Computationally Efficient ◽  
Extensive Evaluation ◽  
Segmentation Accuracy ◽  
Alternative Techniques ◽  
High Recognition Accuracy ◽  
Continuous Dynamic

We present Machete, a straightforward segmenter one can use to isolate custom gestures in continuous input. Machete uses traditional continuous dynamic programming with a novel dissimilarity measure to align incoming data with gesture class templates in real time. Advantages of Machete over alternative techniques is that our segmenter is computationally efficient, accurate, device-agnostic, and works with a single training sample. We demonstrate Machete’s effectiveness through an extensive evaluation using four new high-activity datasets that combine puppeteering, direct manipulation, and gestures. We find that Machete outperforms three alternative techniques in segmentation accuracy and latency, making Machete the most performant segmenter. We further show that when combined with a custom gesture recognizer, Machete is the only option that achieves both high recognition accuracy and low latency in a video game application.

Ion-gating synaptic transistors with long-term synaptic weight modulation

2021 ◽  
Vol 9 (16) ◽  
pp. 5396-5402
Author(s):  
Youngjun Park ◽  
Min-Kyu Kim ◽  
Jang-Sik Lee
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Synaptic Weight ◽  
Recognition Accuracy ◽  
Artificial Neural ◽  
High Recognition Accuracy

This paper presents synaptic transistors that show long-term synaptic weight modulation via injection of ions. Linear and symmetric weight update is achieved, which enables high recognition accuracy in artificial neural networks.

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