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Real-Time Arial targets Tracking for Anti-UAV Systems Using OpenCV 13

Real-Time Arial Targets Tracking for Anti-UAV Systems Using ...

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13th International conference on Electrical Engineering, ICEENG 2022

作者： Dewedar, Mohamed Alaa Kamel, Ahmed Mohsen Hassan, Mohamed Abozied Elhalwagy, Yehia Z. Military Technical College Guidance Department Cairo Egypt

ISBN: (纸本)9781665435093

Recently, Unmanned Aerial Vehicles (UAVs) have been widely used in numerous applications nevertheless in military operations. therefore, there is a crucial need to develop various mechanisms to confront these UAVs. For this reason, the counter system must first determine targets' locations then track them. thus, Accurate target tracking is an important demand in the drone defense system. Because of the significant advancement in computers and software processors, it is now possible to employ these current programs in the operations of positioning and tracking. In this paper, an Open-Source Computer Vision (OpenCV) based drone tracking system is implemented as a tracking system. the performance of the proposed technique is investigated and analyzed through a comparative analysis using a small data set and its ground-truth generated by the annotation tool. the evaluation parameters presented in this study are precision percentage and time analysis. the simulation results present a tradeoff between the accuracy and execution time for the used techniques. © 2022 IEEE.

关键词： Electrical engineering Computers Computer vision Target tracking Program processors Military computing Simulation

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Extracting Sinusoidal Frequency Modulation signal Signature Using Fourier-Bessel Transform and Short-Time Fraction Fourier Transform 6

Extracting Sinusoidal Frequency Modulation Signal Signature ...

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6th International conference on Information Communication and signal processing, ICICSP 2023

作者： Ma, Xiaomeng Gao, Meiguo School of Information and Communication Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350339994

A method using Fourier-Bessel transform (FBT) and short-time fraction Fourier transform (ST-FRFT), for accurate and robust parameter estimate and separation of sinusoidally frequency modulated (SFM) nonstationary signal, is presented. the rotation micro-motion coupling with the rigid body's motion provides an approximately sinusoidal phase modulation, which is known as the micro-Doppler (m-D) signal. According to priori research, the SFM signal shows the nearly linear frequency modulation characteristic in the local part and sparsity features in fraction domain. Firstly, in order to extract the slope of nearly FM and the rotation target's angular frequency, here using FBT method can decompose the rotation target's echo into stationary and nonstationary component. then, using ST-FRFT method separate the target's signal into the fraction domain where through peak search to catch the SFM signal's slope and rotation angular frequency. this function shows enormous potential in solving the problem that nonstationary component always can't be extracted because of weak echo amplitude. the rotation angle frequency is an important characteristic of the rotating target, which shows great significance to the classification of low-altitude and low-speed small targets. this proposed method has been demonstrated by the air target's simulation data which shows effective results of estimating these parameters. © 2023 IEEE.

关键词： Fourier-Bessel transform (FBT) fraction Fourier transform (FRFT) sinusoidal frequency modulation (SFM) unmanned aerial vehicle (UAV)

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Self-supervised Learning as a Means to Reduce the Need for Labeled data in Medical Image Analysis 30

Self-supervised Learning as a Means to Reduce the Need for L...

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30th European signal processing conference (EUSIPCO)

作者： Bencevic, Marin Habijan, Marija Galic, Irena Pizurica, Aleksandra JJ Strossmayer Univ Osijek Fac Elect Engn Comp Sci & Informat Technol Osijek Croatia Univ Ghent Fac Engn & Architecture FELIN GAIM Ghent Belgium

ISBN: (纸本)9789082797091

One of the largest problems in medical image processing is the lack of annotated data. Labeling medical images often requires highly trained experts and can be a time-consuming process. In this paper, we evaluate a method of reducing the need for labeled data in medical image object detection by using self-supervised neural network pretraining. We use a dataset of chest X-ray images with bounding box labels for 13 different classes of anomalies. the networks are pretrained on a percentage of the dataset without labels and then fine-tuned on the rest of the dataset. We show that it is possible to achieve similar performance to a fully supervised model in terms of mean average precision and accuracy with only 60% of the labeled data. We also show that it is possible to increase the maximum performance of a fully-supervised model by adding a self-supervised pretraining step, and this effect can be observed with even a small amount of unlabeled data for pretraining.

关键词： contrastive learning deep learning medical image processing object detection self-supervised learning

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Imaging Detection and Classification for Near-space Dim targets 7

Imaging Detection and Classification for Near-space Dim Targ...

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7th International conference on Vision, Image and signal processing, ICVISP 2023

作者： Yuan, Yupu Xi, Runping Chen, Qian School of Computer Science Northwestern Polytechnical University Xi'an China Shaanxi Province Key Laboratory of Speech & Image Processing Xi'an China National Engineering Laboratory of Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an China

ISBN: (纸本)9781839539848

the motion mode of near-space targets is complex due to their high threat level. the target imaging faces low SNR and susceptibility to background noise. the existing detection and classification algorithms struggle to meet the demands of performance and speed on such targets. To address these issues, a YOLOv5-based target detection algorithm utilizes CARAFE upsampling and the small detection head to extract internal features and edge information of dim target. the algorithm adeptly captures dim target features, enhancing real-time performance and detection accuracy while overcoming subtle feature granularity and extraction difficulties. Aiming at the problem that there is no existing trajectory classification algorithm in the near-space field, we propose an infrared dim target trajectory classification algorithm. the improved attention module is used to enhance the extraction of motion features, and the target category is accurately identified by the prediction module, improving the capacity to extract dependencies between trajectory fragments. We execute comparative experiments on common infrared dim target dataset, near-space infrared dim target dataset and real dataset to confirm the viability of our methods. the outcomes indicate that our proposed method can be applied to the above three types of dim targets and significantly outperforms state-of-the-art methods. © the Institution of Engineering & Technology 2023.

关键词： Extraction

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SCENE TEXT SEGMENTATION BY PAIRED data SYNthESIS 30

SCENE TEXT SEGMENTATION BY PAIRED DATA SYNTHESIS

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30th IEEE International conference on Image processing (ICIP)

作者： Quang-Vinh Dang Lee, Guee-Sang Chonnam Natl Univ Dept Artificial Intelligence Convergence Gwangju South Korea

ISBN: (纸本)9781728198354

Scene text segmentation task has numerous practical applications. However, the number of images in the available datasets for scene text segmentation is too small to effectively train deep learning-based models, leading to limited performance. To solve this problem, we perform the segmentation in two aspects: paired data synthesis and methodology. the former is executed via the proposed Text Image-conditional GANs to generate realistic paired data. We exploit real-world images by self-supervised pre-training scheme via inpainting approach before training the proposed GANs to produce realistic synthetic data. the latter is carried out by the proposed scene text segmentation network to optimize learning the generated paired data, called Multi-task Cascade Transformer. It includes two auxiliary tasks and one main task for text segmentation. the functions of the two auxiliary tasks are to learn the text region to focus on, together with learning the structure of text through their fonts, and then they support the main task. We implement three publicly available datasets for scene text segmentation: ICDAR13 FST, Total Text, and TextSeg datasets to demonstrate the effectiveness of our method. Our experimental result outperforms existing methods.

关键词： Scene Text Segmentation Paired data Synthesis GANs Transformer Multi-task Cascade

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Localized Target Detection Algorithms in Low Illumination Wide-Field of View High-Resolution Environments 24

Localized Target Detection Algorithms in Low Illumination Wi...

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4th International conference on Artificial Intelligence, Big data and Algorithms, CAIBDA 2024

作者： Xie, Chengyou Huang, Xiaohui Jia, Zhenhong Song, Sensen Wang, Jiajia Zhou, Gang Shi, Fei Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi Xinjiang China

ISBN: (纸本)9798400710247

To solve the problem of difficult detection of small targets in wide-field videos captured by eagle-eye equipment in low-illumination environments. we propose a local moving target detection algorithm based on sample consistency. First, the initial background model is established using the first ten frames of the video. the contrast and texture features between the current and background frames are calculated, and the S component in the HSV space is extracted and fused to obtain the final features. Finally, the target area is located through the global segmentation map and the local tracking detection module is activated. Experimental results demonstrate that Compared to other algorithms, the proposed algorithm reduces the running time by implementing local detection. It effectively minimizes the interference caused by light sources and dark spot noise in low-light environments, thereby enhancing the accuracy of the algorithm. © 2024 Copyright held by the owner/author(s).

关键词： Object detection

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Pulsation artifact removal from intra-operatively recorded local field potentials using sparse signal processing and data-specific dictionary

Pulsation artifact removal from intra-operatively recorded l...

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45th Annual International conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Swamy, Chandra Prakash Besheli, Behrang Fazli Branco, Luciano R. F. Provenza, Nicole R. Sheth, Sameer A. Goodman, Wayne K. Viswanathan, Ashwin Ince, Nuri Firat Univ Houston Dept Biomed Engn Houston TX 77004 USA Baylor Coll Med Dept Neurosurgery Houston TX USA Baylor Coll Med Menninger Dept Psychiat & Behav Sci Houston TX USA

ISBN: (纸本)9798350324471

Neural recordings frequently get contaminated by ECG or pulsation artifacts. these large amplitude components can mask the neural patterns of interest and make the visual inspection process difficult. the current study describes a sparse signal representation strategy that targets to denoise pulsation artifacts in local field potentials (LFPs) recorded intraoperatively. To estimate the morphology of the artifact, we first detect the QRS-peaks from the simultaneously recorded ECG trace as an anchor point. After the LFP data has been epoched with respect to each beat, a pool of raw data segments of a specific length is generated. Using the K-singular value decomposition (K-SVD) algorithm, we constructed a data-specific dictionary to represent each contaminated LFP epoch in a sparse fashion. Since LFP is aligned to each QRS complex and the background neural activity is uncorrelated to the anchor points, we assumed that constructed dictionary will be formed to mainly represent the pulsation artifact. In this scheme, we performed an orthogonal matching pursuit to represent each LFP epoch as a linear combination of the dictionary atoms. the denoised LFP data is thus obtained by calculating the residual between the raw LFP and its approximation. We discuss and demonstrate the improvements in denoised data and compare the results with respect to principal component analysis (PCA). We noted that there is a comparable change in the signal for visual inspection to observe various oscillating patterns in the alpha and beta bands. We also see a noticeable compression of signal strength in the lower frequency band (<13 Hz), which was masked by the pulsation artifact, and a strong increase in the signal-to-noise ratio (SNR) in the denoised data.

关键词： Neurons

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Cost-Benefit Analysis of Metavariable Variation in Convolutional Autoencoders Applied to Acoustic Backscattering data from small Underwater targets

Cost-Benefit Analysis of Metavariable Variation in Convoluti...

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OCEANS Hampton Roads conference

作者： Linhardt, Timothy Sen Gupta, Ananya Univ Iowa Dept Elect & Comp Engn Iowa City IA 52242 USA

ISBN: (数字)9781665468091

ISBN: (纸本)9781665468091

With an end goal of physically interpretable results, we build on progress towards finding generalized low dimension encodings of acoustic backscattering data from sonar measurements of small targets submerged in water. the data set in use is TREX-13. the initial effort succeeded in learning non-invertible mappings (encodings) of the data to a low-dimensional vector space with convolutional autoencoders and sparse convolutional autoencoders. the prior work largely used default values for various "metavariables" according to the Keras and Tensorflow library specifications. Mitigating the failure rate of network training as well as ancillary increases in signal reconstruction accuracy and network training speed are the goals of our current efforts. the metavariables in question are the number of convolutional kernels in each network layer, the layer count, the size of the convolutional kernels, the technique for randomly initializing the values of the kernels, and the choice of nonlinear activation functions between network layers. We perform some cost benefit analysis for the modifications in terms of training time, reconstruction accuracy, and GPU memory utilization, and decide whether to continue using the variations in further research.

关键词： machine learning autoencoder signal processing metavariable acoustics

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AutoInit: Analytic signal-Preserving Weight Initialization for Neural Networks 37

AutoInit: Analytic Signal-Preserving Weight Initialization f...

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37th AAAI conference on Artificial Intelligence (AAAI) / 35th conference on Innovative Applications of Artificial Intelligence / 13th Symposium on Educational Advances in Artificial Intelligence

作者： Bingham, Garrett Miikkulainen, Risto Univ Texas Austin Austin TX 78712 USA Cognizant AI Labs San Francisco CA 94105 USA

ISBN: (纸本)9781577358800

Neural networks require careful weight initialization to prevent signals from exploding or vanishing. Existing initialization schemes solve this problem in specific cases by assuming that the network has a certain activation function or topology. It is difficult to derive such weight initialization strategies, and modern architectures therefore often use these same initialization schemes even though their assumptions do not hold. this paper introduces AutoInit, a weight initialization algorithm that automatically adapts to different neural network architectures. By analytically tracking the mean and variance of signals as they propagate through the network, AutoInit appropriately scales the weights at each layer to avoid exploding or vanishing signals. Experiments demonstrate that AutoInit improves performance of convolutional, residual, and transformer networks across a range of activation function, dropout, weight decay, learning rate, and normalizer settings, and does so more reliably than data-dependent initialization methods. this flexibility allows AutoInit to initialize models for everything from small tabular tasks to large datasets such as ImageNet. Such generality turns out particularly useful in neural architecture search and in activation function discovery. In these settings, AutoInit initializes each candidate appropriately, making performance evaluations more accurate. AutoInit thus serves as an automatic configuration tool that makes design of new neural network architectures more robust. the AutoInit package provides a wrapper around TensorFlow models and is available at https://***/cognizant-ai-labs/autoinit.

关键词： Large dataset

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Aerial Photovoltaic Panel Infrared Image Defect Detection Method Based on Improved YOLOv8

Aerial Photovoltaic Panel Infrared Image Defect Detection Me...

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6th International conference on Intelligent Computing and signal processing (ICSP)

作者： Hongtao Hu Yunxiang Li Junfeng Wang Yong Lu School of Computer Science Xi'an ShiYou University Xi'an China No.3 Oil Production Plant PetroChina Changqing Oilfield Company Xi'an Shannxi China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Photovoltaic panels are the core equipment of photovoltaic power generation. Defects in photovoltaic panels are generally detected by analyzing infrared images taken by drones. However, the photovoltaic panel defects to be detected in infrared images are small, and traditional target detection algorithms are not sensitive to small targets. Misdetections and missed detections are prone to occur. In response to the above problems, this article proposes an Improved-YOLOv8 model to increase the accuracy of small target detection. Firstly, the CA attention mechanism is integrated into the YOLOv8 model to improve the ability to extract defect target features and locations; secondly, the Bidirectional Feature Pyramid Network (BiFPN) is used to replace Concat to reduce the loss of target information and enhance the model's ability to fuse targets of different scales. Maintain the sensitivity of the model to targets of different scales; finally, experiments were conducted using infrared image data of photovoltaic panels taken aerially from a photovoltaic power station. the results show that the accuracy and mAP@0.5 of the Improved-YOLOv8 model reached 88.5% and 95.2% respectively, effectively improving the YOLOv8 model's ability to handle small target defects in photovoltaic panels.

关键词： Photovoltaic systems Accuracy Attention mechanisms signal processing algorithms Object detection signal processing Feature extraction data models Drones Defect detection

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