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An automatic eddy detection algorithm based on quadrant angle of velocity vector and its application

INTERNATIONAL JOURNAL OF REMOTE SENSING 2025年第2期46卷 606-635页

作者： Wang, Yong Zhang, Shengjun Kong, Xiangxue Wang, Wanning Northeastern Univ Sch Resources & Civil Engn Shenyang Liaoning Peoples R China Liaoning Earthquake Agcy Liaoning Earthquake Emergency Serv Shenyang Liaoning Peoples R China

Eddies assume a pivotal role in both the oceanic heat cycle and marine dynamic processes. The development of real-time satellite observations, coupled with advancements in computer intelligence, has propelled automatic eddy detection algorithms to the forefront of ocean remote sensing research. Nevertheless, target omissions and accuracy degradations are inevitable for multiple detection algorithms due to strong morphological variations in ocean eddies. This paper proposes an automatic detection algorithm based on the quadrant angle of the velocity vector in the flow field. Firstly, a rectangular search box is established, and the corresponding quadrant angles at the four vertices are calculated. Secondly, the centres and types of eddies are determined according to the cumulative sum and variety rules of quadrant angles. Then the outermost boundary is identified by regularity of quadrant angles in eight directions expanding outward from the centre of the eddy using the stream function equation. The new algorithm is assessed and verified using geostrophic flow data derived from the CMEMS standard gridded sea level anomaly product. Furthermore, its detection capabilities are demonstrated through a comparative analysis with several other algorithms. All methods exhibit consistent efficacy in detecting the majority of eddies with similar spatial distributions. In cases where the new algorithm identifies a specific eddy not detected by the FF15 and ND10 algorithms, sea surface temperature data is employed for verification. The sea surface temperature distribution map, along with the obtained results, illustrates the superiority of the new algorithm and its adaptability to products with varying resolutions. Additionally, the results undergo verification through a manual detection method, revealing that the new algorithm achieves SDR of 91.73% and an EDR of 3.54%. This percentage significantly surpasses the lower acceptable limit of 80% for the SDR parameter. The new algor

关键词： Ocean eddy detection algorithm Sea level anomaly Quadrant angle SWOT

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CNN Aided detection algorithm for Uplink Multiuser Modulation Encrypted Massive MIMO Systems

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第3期73卷 3786-3803页

作者： Li, Lin Hou, Huijun Meng, Weixiao Nanjing Univ Posts & Telecommun Sch Internet Things Jiangsu Key Lab Broadband Wireless Commun & Intern Minist Educ Nanjing 210046 Peoples R China CETC 14th Res Inst Nanjing 210012 Peoples R China Harbin Inst Technol Commun Res Ctr Harbin 150001 Peoples R China

In the traditional physical layer encryption methods, both the delay and errors brought by the keys generation and interaction and those from the channel estimation are too high to be employed for the uplink multiuser massive multiple-input and multiple-output (MIMO) systems. Differently, this paper constructs a lightweight encryption scheme with the modulation random chaotic encryption (MRCE) signals in the uplink massive MIMO systems where the keys are not required to be known, a priori, at the base station (BS). Their generation is off-line and does not employ the channel state information, and there is no immediate interaction. Specially, as a deep learning based solution for detecting the MRCE signals in the uplink massive MIMO systems, the convolutional-neural-network aided nonlinear detection (CAD) algorithms are proposed in this paper. The simulation results showed their effectiveness. The anti-eavesdrop ability of the MRCE signals is verified even against eavesdroppers equipped with multiple antennas at high average signal to noise ratio (SNR). As shown in the simulation results, when the BS does not previously know the keys, the proposed CAD algorithms have a much better bit error rate (BER) performance and higher secrecy spectral efficiency (SSE) than the less efficient ones of their corresponding unassisted methods. The BER is closer to the theoretical lower bound of the optimum value obtained by the maximum likelihood method. They require lower average received SNR to converge to the theoretical maximum SSE given by the no error transmissions from the legitimate users to the BS. These performances also approach those of their corresponding unassisted algorithms without prior known keys. The proposed CAD algorithms have medium/strong robustness against the channel estimation error and medium/low polynomial computational complexity.

关键词： Massive multiple-input and multiple-output (MIMO) systems modulation encryption convolutional-neural-network detection algorithm

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Moving object detection algorithm and motion capture based on 3D LiDAR

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OPTICAL AND QUANTUM ELECTRONICS 2024年第4期56卷 585-585页

作者： Jiang, Jian Hunan Univ Arts & Sci Sch Comp & Elect Engn Changde 415000 Peoples R China

The application of autonomous driving technology in the field of transportation has become a hot research direction, and autonomous vehicles need to accurately detect and track moving targets around. As a kind of sensor widely used in the field of automatic driving, LiDAR has the characteristics of high precision and long distance detection. Therefore, this paper adopts a target detection algorithm based on three-dimensional LiDAR, which can identify moving targets accurately. Then the motion path of the detected target is captured and tracked by optical method, and the motion state of the target is monitored in real time. The experimental results show that the moving target detection algorithm and optical motion acquisition method based on 3D LiDAR can detect and track the moving target effectively, and capture its moving trajectory accurately. The application of this method to autonomous vehicles can improve vehicle perception and driving safety, and also provide a useful reference for other fields of moving object detection and tracking research.

关键词： Three-dimensional Laser radar Moving object detection algorithm Motion capture

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A three-stage detection algorithm for automatic crack-width identification of fine concrete cracks

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JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING 2024年第6期14卷 1373-1382页

作者： Huang, Huang Wu, Zhishen Shen, Haifeng Ibaraki Univ Dept Urban & Civil Engn Hitachi 3168511 Japan Southeast Univ Int Inst Urban Syst Engn Nanjing Peoples R China

Semantic image segmentation is extensively used for automatic concrete crack detection. In previous studies on semantic image segmentation, concrete images were usually labeled as crack and noncrack zones, and recognition models were then trained using artificial neural networks. However, there is not enough edge information in concrete images for the trained model to identify effectively fine concrete cracks (widths < 0.1 mm). Furthermore, complex backgrounds in concrete images can cause false detections. To improve efficiency and reduce false detections, this study develops a three-stage automatic crack-width identification method for fine concrete cracks. First, a full crack skeleton information identification based on image segmentation is proposed. The performance of the mainstream image segmentation architectures, PSP-Net, Seg-Net, U-Net, and Res-Unet, are compared and analyzed, demonstrating that the Res-Unet-based crack skeleton segmentation is the most accurate at fine-crack detection and able to solve the information loss problem that occurs when learning the imbalanced data of fine concrete cracks. Second, a fractal dimension (FD)-based false detection removal process is applied to discriminate true cracks and false detections. The results show that false detections (line-like curves, shadows, and surface stains) can be removed, increasing the matching rate from 0.6476 to 0.8351. Finally, the FD features of the crack skeleton with maximum widths < 0.1 mm, crack widths in the range of 0.1-0.2 mm, and crack widths > 0.2 mm are calculated. Findings illustrate that the values of the FD feature for the three crack-width ranges are suitable for quantitative characterization of identified crack widths.

关键词： detection algorithm Image segmentation Concrete crack-width identification Artificial neural network

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Image cutting in video media technology application based on detection algorithm

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2023年 1-13页

作者： Xiong, Jiahui Hunan Univ Arts & Sci Changde 415000 Hunan Peoples R China

Speech activity detection algorithm belongs to a kind of means to judge the audio section and non audio section. Audio information diagnosis is composed of audio and noise section, and special treatment means are used for different signals. In the real model test, we can understand that RBF core has super stable attributes compared with other models, and has good self-renewal ability. In the audio information discrimination attribute diagnosis, we can understand the upgrading calculation process, and understand the large-scale counting audio information discrimination calculation process. The removal of pictograph is the primary obstacle to the solution scope of visual information. In the real use process, the classified noises are stored in the image removal with different attributes on a large scale, which plays a great role in the gradual diagnosis of image removal such as image upgrade and image classification. Therefore, in order to update the image level, eliminate the noise object and gradually diagnose the image, exploring the image removal method has become the first influential step in the first step of image diagnosis. In the era of interconnection, we began to further explore the merging means model of interconnection, visualization and information interoperability, focusing on the governance of the merging and upgrading means model on the premise of interconnection, visualization and information interoperability, We can also apply multi-channel upgrading to upgrade our own hardware and software strength, observe and learn the quality level of the mode and class, so that the practical role of the means of mutual transmission of interconnected visual information can go on for a longer time.

关键词： Voice activity detection algorithm Image cutting Video media technology

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detection algorithm of safety helmet wearing based on deep learning

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第13期33卷 e6234-e6234页

作者： Huang, Li Fu, Qiaobo He, Meiling Jiang, Du Hao, Zhiqiang Wuhan Univ Sci & Technol Coll Comp Sci & Technol Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan Peoples R China Wuhan Univ Sci & Technol Minist Educ Key Lab Met Equipment & Control Technol Wuhan Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan Peoples R China Wuhan Univ Sci & Technol Res Ctr Biomimet Robot & Intelligent Measurement Wuhan Peoples R China Wuhan Univ Sci & Technol Inst Precis Mfg Wuhan Peoples R China

In the production and construction of industry, safety accidents caused by unsafe behaviors of staff often occur. In a complex construction site scene, due to improper operations by personnel, huge safety risks will be buried in the entire production process. The use of deep learning algorithms to replace manual monitoring of site safety regulations is a powerful guarantee for sticking to the line of safety in production. First, the improved YOLO v3 algorithm is used to output the predicted anchor box of the target object, and then pixel feature statistics are performed on the anchor box, and the weight coefficients are respectively multiplied to output the confidence of the standard wearing of the helmet in each predicted anchor box area, according to the empirical threshold determine whether workers meet the standards for wearing helmets. Experimental results show that the helmet wearing detection algorithm based on deep learning in this paper increases the feature map scale, optimizes the prior dimensional algorithm of specific helmet dataset, and improves the loss function, and then combines image processing pixel feature statistics to accurately detect whether the helmet is worn by the standard. The final result is that mAP reaches 93.1% and FPS reaches 55 f/s. In the helmet recognition task, compared to the original YOLO v3 algorithm, mAP is increased by 3.5% and FPS is increased by 3 f/s. It shows that the improved detection algorithm has a better effect on the detection speed and accuracy of the helmet detection task.

关键词： construction site scene deep learning detection algorithm safety helmet YOLO v3

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A Defect detection algorithm of Round Tablets Based on Image Information 9

A Defect Detection Algorithm of Round Tablets Based on Image...

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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Zhang, Lianqi Zhou, Rui Sun, Yuan Chen, Xiaojin Yang, Zhiwang College of Electrical Engineering and Automation Xiamen University of Technology Fujian Xiamen China Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control Fujian Xiamen China

ISBN: (纸本)9798350376548

This paper presents a novel circular tablet defect detection algorithm based on machine learning techniques to address the challenge of insufficient data for training samples. Utilizing cameras, lenses, and light sources, we establish an image acquisition hardware system for capturing tablet images, and on the basis of image preprocessing, the designed defect detection algorithm is used for detection, forming a complete and efficient round tablet defect detection process. The experimental results have been demonstrated that the performance of the sample missed detection rate was 0.2%, the false detection rate is 0.6%, and the total error rate is 0.8%, all lower than 1%. The proposed detection algorithm does not require sample training and can be well applied to the identification of defects such as abnormal tablet circular areas and foreign matter on the surface of tablets. © 2024 IEEE.

关键词： detection algorithm Image information Tablet defects

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YOLOX-Dense-CT: a detection algorithm for cherry tomatoes based on YOLOX and DenseNet

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JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022年第6期16卷 4788-4799页

作者： Zheng, Hao Wang, Guohui Li, Xuchen Xian Technol Univ Sch Optoelect Engn Xian 710021 Peoples R China

The detection of cherry tomatoes has a great significance for robotic harvesting. However, uneven environment conditions, such as branch and leaf occlusion, cluster of fruits, and so on, have made the cherry tomatoes detection very challenging. This paper proposes an effective cherry tomato detection algorithm called YOLOX-Dense-CT to solve these problems. To be specific, the DenseNet network is treated as the basic backbone of the original YOLOX to make the whole network suitable for the cherry tomatoes. Moreover, the convolutional block attention module (CBAM) attention mechanism is applied to make the features from the backbone more fused with the Neck part. As suggested by the experimental results, the proposed YOLOX-Dense-CT model is effective in detecting cherry tomatoes, with the mean average precision (mAP) reaching 94.80%, which is 4.02% higher than the original YOLOX-L model. Meanwhile, the number of parameters is only 34.6 M, which is 19.6 M lower than the YOLOX-L. Furthermore, the YOLOX-Dense-CT is compared to general target detection models and it has the best detection performance. In summary, the proposed method can well meet the requirements of high accuracy detection and provide a strategy for the cherry tomato detection system.

关键词： Cherry tomatoes Robotic harvesting detection algorithm YOLOX DenseNet

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Video adaptive watermark embedding and detection algorithm based on phase function equation

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2022年第1期8卷 1445-1454页

作者： Chao, Yan Wang, Hao Liu, Shuying Shorman, Samer Fuyang Normal Univ Sch Comp & Informat Engn Fuyang 236037 Anhui Peoples R China Appl Sci Univ Coll Arts & Sci Eker Bahrain

In order to improve the video watermark embedding strength and balance the transparency of the watermark system, this paper proposes a combination of all phase Biorthogonal Transform and watermark embedding to complete the experiment. Firstly, the all phase biorthogonal transform is described. Referring to the construction process of APBT, combined with APDF and DST, a new all phase biorthogonal transform, all phase discrete sinusoidal biorthogonal transform (APDSBT), is proposed. This paper makes full use of MPEG-2 compression format to embed watermark directly in DCT domain. The low-frequency coefficients in the DCT block of the brightness space of I frame are selected as the watermark embedding space. The brightness component of each image block is transformed by two-dimensional DCT in the unit of 8x8 image blocks. By introducing the idea of energy receiver into the detection of digital watermark, the following correlation detector can be obtained. Implemented with MATIAB and VC + +. In the experiment, the foreman video test sequence is used as the watermark carrier, and the copyright logo image designed by ourselves is used as the watermark image to test the video watermarking system. The results show that when the coding rate is 3Mvps, the accuracy of each plane is 100%, 98.48%, 98.12% and 96.27% respectively. When the coding rate is 2.6Mvps, the accuracy of each plane is 100%, 96.38%, 94.87% and 95.21% respectively. This algorithm selects the frame of video to embed watermark. On the premise of ensuring video quality, this algorithm is robust to common video watermark attacks (MPEG compression, frame loss, frame clipping and frame rearrangement).

关键词： Phase function Video Watermark embedding detection algorithm Coding rate

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Research on Rapid detection algorithm of Electronic Components Based on Deep Learning 3

Research on Rapid Detection Algorithm of Electronic Componen...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Jun, Lu Yuansheng, Cheng Wuhan Donghu University Hubei Wuhan430212 China Hydrology and Water Resources Survey Bureau of Middle Yangtze River Changjiang Water Resources Commission Hubei Wuhan430012 China

ISBN: (纸本)9798350319125

The detection of electronic components plays an important role in improving the intelligence level of the manufacturing industry, but there is a problem of poor detection accuracy. Neural network algorithms cannot improve the detection accuracy and detection speed of electronic components. Therefore, this paper proposes a deep learning algorithm to detect electronic components. Firstly, deep separable convolution is introduced to reduce the overall complexity of the model. Then, abstract features are extracted through the efficient feature extraction network, and the pre-selection box is obtained by using the regional proposal network to complete the detection of electronic components. /b11>The MATLAB simulation results show that the algorithm proposed in this paper can effectively improve the accuracy and efficiency of electronic component detection. © 2023 IEEE.

关键词： /b10>High-precision two-stage detection model deep learning detection algorithm electronic components

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