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A method for constructing a recognition algorithm in algebra over an estimate calculation set

Computational Mathematics and Mathematical Physics 2007年第8期47卷 1368-1372页

作者： Romanov, M.Yu. Moscow Institute of Physics and Technology Dolgoprudnyi Moscow oblast 141700 Institutskii per. 9 Russian Federation

关键词： Algebra over an estimate calculation set recognition algorithm

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Research on Wall Crack recognition algorithm Based on YOLOv8 4

Research on Wall Crack Recognition Algorithm Based on YOLOv8

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4th IEEE International Conference on Data Science and Computer Application, ICDSCA 2024

作者： Gao, Pengyuan Liu, Lijie Song, Xinzhe Wang, Yubo Wu, Siting Chi, Ruyue Heilongjiang Bayi Agricultural University Daqing City China

ISBN: (纸本)9798350368239

In order to overcome the low efficiency of digital image processing, this paper introduces DNN to detect wall cracks. By constructing the training data set of deep learning model, the method of test image acquisition and preprocessing is studied, and the test image library of crack detection model is established. The crack open data set is used to improve the crack training. The deep learning target detection algorithm was studied, and the wall crack recognition and positioning model based on YOLOv8 algorithm was constructed. The width, depth parameters and boundary frame loss function of YOLOv8 algorithm were optimized and adjusted by using the crack recognition data set. It effectively reduces the risk and cost of detection work, and has a strong engineering application prospect and social value. ©2024 IEEE.

关键词： Deep learning recognition algorithm Wall cracks YOLOv8 algorithm

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Study on different touch object recognition algorithm based on Android and capacitive touch panel

Study on different touch object recognition algorithm based ...

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10th International Conference on Sensing Technology (ICST)

作者： Zhu Qiwen Sun Jun Huang Xueqin Zhou Hongmei Tang Yongming Southeast Univ Sch Elect Sci & Engn Nanjing Jiangsu Peoples R China Nanjing JieNuo Environm Technol Ltd Co Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781509007967

Nowadays, touch panel technology has become the mainstream of human-computer interaction. Based on a smart phone equipped with Atmel's latest touch chip-maxTouch874 and the Eclipse software, a system that can gather the original signals from self-capacitance and mutual-capacitance is developed. The recognition of four kinds of touch objects is achieved with the different characteristics of different touch objects. The test result verifies the accuracy of the algorithm.

关键词： Touch panel Different touch object Self-capacitance Mutual-capacitance recognition algorithm

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A new recognition algorithm of 2-D objects

A new recognition algorithm of 2-D objects

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3rd International Conference on Signal Processing (ICSP 96)

作者： Du, XP Shi, L Gu, DR Department of Electronic Engineering University of Electronic Science and Technology Chengdu Sichuan China

ISBN: (纸本)7505338900

In this paper, we present a new algorithm to recognize a two- dimensional object of general shape which has a closed contour, The algorithm includes two stages. The first stage consists of building a one-dimensional signal representation of the two-dimensional boundary of the object and obtaining the wavelet transform of the representation by using a particular mother wavelet which is the first derivative of a smoothing function. The second stage consists of calculating the rise -fall periods of the wavelet transform and its transfer probability matrix as the feature template, and then completing the matching procedure for objects recognition.

关键词： object recognition wavelet transforms smoothing methods matrix algebra probability feature extraction image matching signal representation recognition algorithm 2D objects two-dimensional object closed contour object shape recognition one-dimensional signal representation two-dimensional boundary wavelet transform mother wavelet smoothing function rise-fall periods transfer probability matrix feature template matching procedure object recognition

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A new characterization and a recognition algorithm of Lucas cubes

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DISCRETE MATHEMATICS AND THEORETICAL COMPUTER SCIENCE 2013年第3期15卷 31-40页

作者： Taranenko, Andrej Univ Maribor Fac Nat Sci & Math SLO-2000 Maribor Slovenia

Fibonacci and Lucas cubes are induced subgraphs of hypercubes obtained by excluding certain binary strings from the vertex set. They appear as models for interconnection networks, as well as in chemistry. We derive a characterization of Lucas cubes that is based on a peripheral expansion of a unique convex subgraph of an appropriate Fibonacci cube. This serves as the foundation for a recognition algorithm of Lucas cubes that runs in linear time.

关键词： Lucas cubes characterization recognition algorithm

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A recognition algorithm TO DETECT PIPE WELD DEFECTS

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2017年第6期24卷 1969-1975页

作者： Cui, Wei Wang, Ke Zhang, Qiang Zhang, Peng Northeast Petr Univ Daqing High Tech Dev Zone Univ St 99 Daqing City Heilongjiang Peoples R China Petrochina Daqing Petrochem Co Mobile Equipment Dept Daqing City Heilongjiang Peoples R China

Taking magnetic flux leakage (MFL) imaging of pipe weld defects as the research object, a weld defect image recognition algorithm based on grey-gradient co-occurrence matrix (GGCM) and cluster analysis and mathematical morphology is proposed. recognition of different types of welding defects was achieved. Firstly, a continuous non-contact scanning MFL system for the pipe weld was used to collect the three-dimensional MFL. Secondly, the three-dimensional MFL signal was converted to a two-dimensional greyscale image. Then the MFL image characteristics of the two-dimensional grayscale image were extracted using GGCM. Based on extracted image features, the characteristic quantity was analysed by using k-means clustering and then through the combination of histogram equalization, Otsu's method of binaryzation, morphologically removing small objects, edge detection, and then structuring a morphologically optimized edge extraction method for edge detection on the grayscale. Through combination of several methods, a new algorithm to improve the detection effect was structured. The results indicated that this algorithm is adaptable and practical. This algorithm solved difficulties associated with the MFL method being used in the weld testing to realize the recognition of pipe weld defects and break through the applicable limitations of traditional signal processing technology.

关键词： defects magnetic flux leakage imaging pipe weld recognition algorithm

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Research on Apple recognition algorithm in Complex Orchard Environment Based on Deep Learning

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SENSORS 2023年第12期23卷 5425-5425页

作者： Zhao, Zhuoqun Wang, Jiang Zhao, Hui Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Tianjin Sino German Univ Appl Sci Sch Mech Engn Tianjin 300350 Peoples R China Tianjin Univ Technol Sch Elect Engn & Automat Tianjin 300384 Peoples R China

In the complex environment of orchards, in view of low fruit recognition accuracy, poor real-time and robustness of traditional recognition algorithms, this paper propose an improved fruit recognition algorithm based on deep learning. Firstly, the residual module was assembled with the cross stage parity network (CSP Net) to optimize recognition performance and reduce the computing burden of the network. Secondly, the spatial pyramid pool (SPP) module is integrated into the recognition network of the YOLOv5 to blend the local and global features of the fruit, thus improving the recall rate of the minimum fruit target. Meanwhile, the NMS algorithm was replaced by the Soft NMS algorithm to enhance the ability of identifying overlapped fruits. Finally, a joint loss function was constructed based on focal and CIoU loss to optimize the algorithm, and the recognition accuracy was significantly improved. The test results show that the MAP value of the improved model after dataset training reaches 96.3% in the test set, which is 3.8% higher than the original model. F1 value reaches 91.8%, which is 3.8% higher than the original model. The average detection speed under GPU reaches 27.8 frames/s, which is 5.6 frames/s higher than the original model. Compared with current advanced detection methods such as Faster RCNN and RetinaNet, among others, the test results show that this method has excellent detection accuracy, good robustness and real-time performance, and has important reference value for solving the problem of accurate recognition of fruit in complex environment.

关键词： target detection recognition algorithm joint loss function soft NMS algorithm global and local characteristics

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Citrus greening disease recognition algorithm based on classification network using TRL-GAN

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2022年 200卷

作者： Xiao, Deqin Zeng, Ruilin Liu, Youfu Huang, Yigui Liu, Junbing Feng, Jianzhao Zhang, Xinglong South China Agr Univ Coll Math & Informat Guangdong Peoples R China Key Lab Smart Agr Technol Trop South China Minist Guangdong Peoples R China Guangzhou Natl Modern Agr Ind Sci & Technol Innova Guangdong Peoples R China

The monitoring and prevention and control of citrus yellow dragon disease is a significant measure to ensure citrus production. If yellow dragon disease appears in citrus orchards, it will cause root rot, fruit deformation and wilting of fruit trees, which will eventually spread to every fruit tree in the whole orchard and cause the death of fruit trees, so it is very meaningful to detect the symptoms of citrus yellow dragon disease early and take appropriate treatment and prevention measures. Pratically, the orchard owner will remove the corresponding fruit trees as soon as they are found to be infected with Huanglong disease, so that it is extremely problematic to obtain a large number of Huanglong disease leaf data. Meanwhile, due to the uncertainty of the pathological trait distribution of citrus yellow dragon disease leaves and the extreme shortage of data, the convolutional neural network model learned in a small number of samples is not capable of generalization. In order to improve the accuracy and generalization of Citrus Greening Disease recognition algorithm, this paper introduces Texture Reconstruction Loss CycleGAN(TRL-GAN) to generate citrus diseased leaf data in realistic scene to increase the richness of samples, and thus proposes the Recognizing Citrus Greening Based on TRL-GAN(RCG TRL-GAN). This algorithm firstly performs background culling by using the instance segmentation network Mask RCNN for realistic scenes citrus yellow dragon disease mottled, zinc deficiency, magnesium deficiency, leaf veins yellowing and other corresponding symptomatic leaves, then introduces texture reconstruction loss improvement Cycle-GAN as training and migrates the diseased leaf style to ordinary green leaves for data expansion, and finally uses the expanded dataset to train the convolutional neural network. Experimental results on the constructed dataset of 4516 images (762 mottled, 749 Zn deficient, 737 Mg deficient, 721 Vein yellowing, 783 Diachyma yellowing, 764 g

关键词： Citrus greening disease recognition algorithm Adversarial network generation Texture reconstruction CNN

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Automated Field-of-View, Illumination, and recognition algorithm Design of a Vision System for Pick-and-Place Considering Colour Information in Illumination and Images

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SENSORS 2018年第5期18卷 1656-1656页

作者： Chen, Yibing Ogata, Taiki Ueyama, Tsuyoshi Takada, Toshiyuki Ota, Jun Univ Tokyo Dept Precis Engn Tokyo 1138656 Japan Univ Tokyo Ctr Engn RACE Res Artifacts Chiba 2778568 Japan Denso Wave Inc Aichi 4702298 Japan

Machine vision is playing an increasingly important role in industrial applications, and the automated design of image recognition systems has been a subject of intense research. This study has proposed a system for automatically designing the field-of-view (FOV) of a camera, the illumination strength and the parameters in a recognition algorithm. We formulated the design problem as an optimisation problem and used an experiment based on a hierarchical algorithm to solve it. The evaluation experiments using translucent plastics objects showed that the use of the proposed system resulted in an effective solution with a wide FOV, recognition of all objects and 0.32 mm and 0.4 degrees maximal positional and angular errors when all the RGB (red, green and blue) for illumination and R channel image for recognition were used. Though all the RGB illumination and grey scale images also provided recognition of all the objects, only a narrow FOV was selected. Moreover, full recognition was not achieved by using only G illumination and a grey-scale image. The results showed that the proposed method can automatically design the FOV, illumination and parameters in the recognition algorithm and that tuning all the RGB illumination is desirable even when single-channel or grey-scale images are used for recognition.

关键词： automated design vision system FOV illumination recognition algorithm

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Evaluation of spoken English self-study system considering speech knowledge recognition algorithm

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2022年第2期8卷 699-710页

作者： Jiang, Mei Xiamen Ocean Vocat Coll Sch Int Business Xiamen 361101 Fujian Peoples R China

This paper builds and evaluates a self-learning system for spoken English pronunciation suitable for PC mobile terminal or mobile terminal users, and the core technology of speech recognition is built on Hidden Markov Model (HMM), which is used to decode the speech signal in spoken English learning. This paper studies the related speech recognition theory and signal processing technology, builds a comprehensive English self-learning system in more complex situations and more user types and conducts a comprehensive evaluation of the system. The results show that the overall accuracy of the HMM model in the spoken English recognition and evaluation system built in this paper is good, and the accuracy of the input audio for people of all ages is greater than 90%. In the younger population, the accuracy of male speech signals was the highest in both closed space and open space, reaching 98.12% and 96.53%, respectively. In addition, it is observed that the accuracy gradually decreases as more wrong judgements are made on the speech input signal. When the evaluation is poor, the accuracy of the scoring results decreases to 55%, and when there are fewer voice judgement errors, it is in the excellent range, where the accuracy reached 88%.

关键词： speech recognition self-learning system recognition algorithm HMM model

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