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small Aerial Target Detection Algorithm Based on Improved YOLOv5 14th

Small Aerial Target Detection Algorithm Based on Improved YO...

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14th International conference on Swarm Intelligence (ICSI)

作者： Yang, TianLe Chen, JinLong Yang, MingHao Guilin Univ Elect Technol Guilin 541004 Guangxi Peoples R China Chinese Acad Sci CASIA Res Ctr Brain Inspired Intelligence BII Inst Automat Beijing 100190 Peoples R China

ISBN: (纸本)9783031366246;9783031366253

small target detection is a difficult problem in the field of target detection. small targets have many characteristics, such as less visual information, difficult to extract discriminative features, easy background confusion and so on, as a result, its detection accuracy is often only half of the large target. To solve these problems, we study small target detection in UAV aerial scene, and propose an improved YOLOv5-based small target detection algorithm. First, we performed two optimizations on the YOLOv5 prediction scale, reducing the missed detection of small targets and reducing model parameters and computational complexity, while reducing the negative impact of scale mismatches. then, based on the idea of BiFPN, we introduce the skip connection from backbone network to PAN, and use Concat weighted fusion instead of the weighted fusion in BiFPN to improve the detection precision of the model. Finally, we do some experiments on the data set of Visdrone2019. the experimental results show that the mAP@0.5 and mAP@0.5:0.95 of the improved model are improved by 6.6% and 4.2%, respectively, the parameters of the model are reduced and YOLOv5 is light weight.

关键词： small target detection YOLOv5 Visdrone2019

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FeatureCut: An Adaptive data Augmentation for Automated Audio Captioning

FeatureCut: An Adaptive Data Augmentation for Automated Audi...

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14th Annual Summit and conference of the Asia-Pacific-signal-and-Information-processing-Association (APSIPA ASC)

作者： Ye, Zhongjie Wang, Yuqing Wang, Helin Yang, Dongchao Zou, Yuexian Peking Univ Sch ECE Shenzhen Peoples R China

ISBN: (数字)9786165904773

ISBN: (纸本)9786165904773

Automated audio captioning (AAC) aims at generating natural language descriptions for an audio clip. Due to the difficulty and high cost of annotating audio-caption pairs, the existing audio captioning dataset is of a very small scale which leads to unsatisfied performance for AAC models. One intuitive and effective solution is to augment training data to boost performance instead of annotating more data. To this end, we propose an online data augmentation method (FeatureCut) incorporating the encoder-decoder framework to enable the language decoder fully make use of the acoustic features in generating the captions. Specifically, we propose to construct a Feature Attention-weight Table (FAT) from the attention-weight maps given by the attention module in the language decoder for each audio clip. that is, the FAT can reflect the importance of each acoustic feature in the decoding process. then a progressive acoustic features cutting strategy is designed to discard the higher magnitude values in FAT to generate augmented acoustic features. We apply Kullback-Leibler divergence (K-L divergence) between original and augmented data to encourage AAC models to make similar predictions from different views of them, in order to balance the learning capability of ACC models. We evaluate our proposed FeatureCut on the Clotho dataset. the experimental results demonstrate that our proposed FeatureCut can be easily plugged into the state-of-the-art ACC models (i.e. MAAC and CNN-Transformer) and can obtain 5.1% and 3.1% gains in CIDEr scores.

关键词： Adaptation models Natural languages Training data Predictive models Tagging Acoustics data models

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Bearing Fault Diagnosis Method Based on RepVGG Migration Diagnosis Network 14

Bearing Fault Diagnosis Method Based on RepVGG Migration Dia...

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14th IEEE Global Reliability and Prognostics and Health Management conference, PHM-Hangzhou 2023

作者： Huang, Yijun Zeng, Zhaoyang Fang, Zhaoyan Zhu, Qingyu Jiang, Jueyi Avic China Aero-Polytechnology Establishment Beijing China

ISBN: (纸本)9798350301359

Given the shortcomings of existing deep learning methods in rolling bearing fault diagnosis, such as a large number of training parameters and complex network, a lightweight neural network-migration diagnosis network model based on RepVGG is proposed. First of all, the vibration signal is converted into a three-channel time-frequency image by the combination of short-time Fourier transform (STFT) and pseudo-color processing technology. then the RepVGG model trained on the Case Western Reserve University (CWRU) data set is taken as the pre-training model, and the methods of intermediate working conditions and small sample migration are introduced and applied to bearing fault diagnosis under other working conditions, the experimental results show that the algorithm has the advantages of fast training speed, high generalization, and high accuracy. Finally, the open source framework ncnn is used to deploy the model to the edge platform Raspberry Pi. the experimental results show that the edge calculation module can effectively diagnose bearing faults and is a qualified motor-bearing fault diagnosis system. © 2023 IEEE.

关键词： Bearing fault diagnosis Deep learning RepVGG small sample transfer learning

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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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17th International conference on Intelligent Information Hiding and Multimedia signal processing, IIH-MSP 2021, in conjunction with the 14th International conference on Frontiers of Information Technology, Applications and Tools, FITAT 2021

17th International Conference on Intelligent Information Hid...

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17th International conference on Intelligent Information Hiding and Multimedia signal processing, IIH-MSP 2021, in conjunction with the 14th International conference on Frontiers of Information Technology, Applications and Tools, FITAT 2021

ISBN: (纸本)9789811910524

the proceedings contain 64 papers. the special focus in this conference is on Intelligent Information Hiding and Multimedia signal processing. the topics include: An Expressway Congestion Detection Algorithm Based on ETC data Mining;A New PPE Algorithm Based on Parallel Communication Strategy;design and Optimization of Electric Vehicle Seat;design and Fault Research of Relay Protection for Shore Power;applying Image processing Technology to Estimate Vehicle Speed;K-PPE: A Variant Phasmatodea Population Evolution Algorithm for Global Optimization;five Phases Algorithm for Global Optimization;an Image Encryption Method Based on Logistic Mapping and Phasmatodea Population Evolution Algorithm;video Surveillance System of Substation Based on AdaBoost Pedestrian Anti-misjudgment Algorithm;transformer Fault Identification Method Based on Improved Roberts Algorithm;Expressway Traffic Speed Prediction Method Based on CNN_Bi-LSTM Model via ETC data;How Consumers’ Lending Knowledge and Self-efficacy Drive their Intention: A Model of TAM Within Information Asymmetric Condition;occluded and Tiny Face Detection with Deep and Shallow Features Fusion and Compensation;simulation and Analysis of Steering Mechanical Characteristics Between Tire and Soil;Study on the Method of Adversarial Example Attack Based on MI-FGSM;virtual Design of Chinese theater Arts in Augmented Reality Media System;design Analysis and Optimization of the Anti-collision Structure of the Four-Wheel Electric Scooter;binary Fish Migration Optimization for Feature Selection Using Time-Varying Transfer Function;ResRnnNet: Learning to Reconstruct the 3D Room Layout from a Single RGB Panorama;design and Optimization of Spring in Damping System of New Energy Electric Vehicle;small Object Detection Based on Multi-source data Learning Fusion Network;preface.

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17th International Conference on Intelligent Information Hid...

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ISBN: (纸本)9789811910562

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Deep Neural Network-based Fingerprinting Localization for 5G NR mmWave small-Cell 14

Deep Neural Network-based Fingerprinting Localization for 5G...

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14th International conference on Information and Communication Technology Convergence, ICTC 2023

作者： Park, Suah Jeong, Minsoo Chung, Hyeonjin Jung, Hongseok Jwa, Hye-Kyung Na, Jeehyeon Kim, Sunwoo Hanyang University Department of Electronic Engineering Seoul Korea Republic of Electronics and Telecommunications Research Institute Daejeon Korea Republic of

ISBN: (纸本)9798350313277

In this paper, we propose a fingerprinting-based localization method in the 5G millimeter-wave (mmWave) smallcell channel. the proposed method uses measurements that do not require additional processes to collect, such as synchronization signal-reference signal received power (SS-RSRP) used for synchronization between the user and base station in 5G communication and transmitter (TX) beam ID data as fingerprint data. With the collected data and a deep neural network (DNN)based pattern matching model, we evaluate the localization performance in 5G small-cell environments. As a result, the proposed method achieves localization root-mean-squared-error (RMSE) of 2.76m, which can be applicable to the actual smallcell environment. © 2023 IEEE.

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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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D²Net: A Denoising and Dereverberation Network Based on Two-branch Encoder and Dual-path Transformer

D<SUP>2</SUP>Net: A Denoising and Dereverberation Network Ba...

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14th Annual Summit and conference of the Asia-Pacific-signal-and-Information-processing-Association (APSIPA ASC)

作者： Wang, Liusong Wei, Wenbing Chen, Yadong Hu, Ying Xinjiang Univ Sch Informat Sci & Engn Urumqi Peoples R China Key Lab Signal Detect & Proc Xinjiang Urumqi Peoples R China

ISBN: (数字)9786165904773

ISBN: (纸本)9786165904773

the simultaneous denoising and dereverberation for single-channel mixture speech under the complicated acoustic environment is considered to be a challengeable task. In this paper, we propose a denoising and dereverberation network named as D(2)Net in which a two-branch encoder (TBE) is designed to extract and selectively fuse features with different granularity. In addition, we design a global-local dual-path transformer (GLDPT) which introduces the local dense synthesizer attention (LDSA) in the dual-path transformer to improve the perception of local information. We evaluated our proposed D(2)Net and conducted ablation studies on the VoiceBank+DEMAND and WHAMR! datasets. Meanwhile, we chose three types of data in the WHAMR! dataset to verify the ability of the D(2)Net on the tasks of denoising-only, dereverberation-only, and simultaneous denoising and dereverberation, respectively. Experimental results show that our proposed model outperforms the comparative models, and all achieve better performance on the tasks of simultaneous denoising and dereverberation, dereverberation-only, and denoising-only, while keeping a small number of network parameters.

关键词： Speech denoising Speech dereverberation Two-branch encoder Dual-path transformer

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Deformation Monitoring Experiment and data Analysis of Beidou Surface Deformation Measuring Radar and GBSAR 14th

Deformation Monitoring Experiment and Data Analysis of Beido...

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14th China Satellite Navigation conference, CSNC 2024

作者： Xu, Zhixiang Liu, Feifeng Wang, Zhanze Zhang, Shuyao Bi, Jiahe Beijing Institute of Technology 5 Zhongguancun South Street Haidian District Beijing China

ISBN: (纸本)9789819969272

the Beidou surface deformation measuring radar can realize the three-dimensional deformation measurement of the whole scene by using a single receiver. Since the system uses an in-orbit satellite as a transmitter, its long-term measurement costs are much lower than GBSAR and space-borne InSAR systems. However, due to the small power of navigation star to earth, the signal to noise ratio of scene echo is low. Based on the comparison between the high precision GBSAR one-dimensional deformation measurement data and the deformation measurement results of the Beidou surface deformation measurement radar repeater, this paper initially verifies the deformation measurement capability of the Beidou surface deformation measurement radar. this paper first introduces the Beidou surface deformation measuring radar, and then lists the PS processing flow of the Beidou surface deformation measuring radar according to the characteristics of the system. Finally, a multi-system joint verification experiment is carried out in Zhujiawan area of Chongqing, and a cross-system measurement accuracy verification method is proposed. Based on the three days’ measurement results, it is proved that GBSAR and Beidou surface deformation measurement radar have the same measurement results, and the three-dimensional deformation measurement results of the strong scattering point are consistent with the actual situation. the experiment proves that the Beidou surface deformation measuring radar has the ability to measure the three-dimensional deformation of the whole scene and can be widely used in disaster prediction. © 2024, Aerospace Information Research Institute.

关键词： Radar measurement

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