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检索条件"主题词=detection algorithms"

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Target detection and Anti-Clutter Using Energy Priority Idea Based on 77-79 GHz Radar

Target Detection and Anti-Clutter Using Energy Priority Idea...

引用

IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Jiadong Pu Shaonan Li Chao Wang Zhiquan Zhou Xiang Feng School of information science and Engineering Harbin Institute of Technology Weihai Shandong China Beijing Institute of Control and Electronics Technology Beijing China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

In this paper, to accurately establish sea clutter model, we propose a 3-partitioned random multiplicative mechanism using multifractal theory to tackle the high-frequency radar sea clutter, which could extract feature factors from sea clutter data and verifies the multifractal features. Furthermore, we present a clutter energy priority SVD-OP (CEP-SVD-OP) based detection algorithm. In which, echo signals of the selected distance units are combined into a signal space, and processed by orthogonal projection idea. Then, the clutter suppression is performed again for all the distance units on the individual pulses after orthogonal projection. The singular values are utilized to determine the signal subspace and clutter subspace. After eliminating the clutter subspace, the signal is reconstructed using the Hankel matrix, and the detection-flow can be achieved. Simulations has demonstrated that CEP-SVD-OP algorithm obtains some superior performance in sea clutter suppression and target detection.

关键词： Radar clutter Signal processing algorithms Sea measurements Radar detection Object detection Millimeter wave radar Feature extraction Fractals Clutter detection algorithms

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An Improved Model for People detection Based on YOLOv8

An Improved Model for People Detection Based on YOLOv8

引用

Information Technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Yang Yuhong Song Zhuo Thelma D. Palaoag Li Shuning CITCS University of the Cordilleras Baguio City Philippines Software College Shanxi Agricultural University Taiyuan China

ISBN: (数字)9798331510732

ISBN: (纸本)9798331510749

People detection is vital for security in large-scale disaster outdoor rescue and large public spaces. Despite its success in various applications, people detection technology continues to face challenges such as low accuracy, difficulty in feature extraction, and slow model performance. This paper proposes an enhanced people detection algorithm based on You Only Look Once version 8 (YOLOv8). The algorithm introduces the Cross Stage Partial Networks with Focused Expansion and Feature Fusion Compressed Information Bottleneck (C2fCIB) module to optimize the backbone network, enhance feature extraction capabilities, and improve the detection accuracy of small and occluded targets, thereby increasing the model's overall detection accuracy. Additionally, a combined loss function incorporating Skew Intersection over Union (SIOU) and Complete Intersection over Union (CIOU) is employed to optimize the detection rate for small and occluded targets, further enhancing accuracy and expediting convergence. Experimental results demonstrate that the average detection accuracy of the improved model increased by 15.4%, and the training speed improved by 15.5%. Consequently, the enhanced model is more effective for people detection, contributing to safety in public spaces and other application scenarios. Furthermore, the model's enhancement methods offer valuable insights for researchers and experts in the field of people detection.

关键词： YOLO Training Accuracy Disasters Feature extraction Security Information technology detection algorithms Faces Convergence

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Small Targets Under Drone Perspective Based on Improved YOLOv5s detection Algorithm

Small Targets Under Drone Perspective Based on Improved YOLO...

引用

Signal Processing and Intelligent Computing (SPIC), International Conference on

作者： Xinyue Feng Jun Liu Zhichao Zhang School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

Research aimed at the detection of ground targets using UAVs for shooting, the phenomenon of missed detections and false detections still exists, and the algorithm cannot simultaneously consider detection accuracy and parameter size. Therefore, this paper uses the improved YOLOv5s algorithm to further study the above problems. Firstly, a Simple Patches of Convolution-Mixer (CSPCM) is added to both the Head and Backbone network structures of the original model, which uses deep convolution to help better fuse the information of the two dimensions and achieve higher accuracy; In addition, a small target detection layer is set up in the neck to expand the perception field of the model, so that the algorithm model can be handy when dealing with small target detection tasks. Secondly, the lightweight convolutional network module GhostNetV2 is used as the backbone of the algorithm, reducing feature redundancy in channels. Finally, YOLOv5-CG was tested on VisDrone2019. The new model reduced the number of parameters by 1468493 and the model size by 2.4M, mAP@0.5 Improved by 4.1%, mAP@0.5 0.95 increased by 1.9%; The experimental results indicate that Yolov5-CG can achieve parameter simplification while ensuring improved accuracy, and can be better used for small object detection.

关键词： YOLO Accuracy Head Fuses Redundancy Signal processing algorithms Neck detection algorithms Testing Drones

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Optimal detection in MIMO Systems Using Spatial Sigma-Delta ADCs

Optimal Detection in MIMO Systems Using Spatial Sigma-Delta ...

引用

Asilomar Conference on Signals, Systems & Computers

作者： Toan-Van Nguyen Sajjad Nassirpour Italo Atzeni Antti Tölli A. Lee Swindlehurst Duy H. N. Nguyen Department of Electrical and Computer Engineering San Diego State University San Diego CA USA Centre for Wireless Communications University of Oulu Finland Department of Electrical Engineering and Computer Science University of California Irvine CA USA

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

The spatial Sigma-Delta architecture can be used to reduce the quantization noise and thus improve the effective resolution of few-bit analog-to-digital converters (ADCs) for certain spatial frequencies of interest. This paper proposes a novel data detection scheme based on the variational Bayes (VB) inference framework for multiple-input multiple-output (MIMO) systems that utilize first-order spatial Sigma-Delta ADCs. We derive a closed-form expression to approximate the posterior distributions of the transmitted data symbols, which are then used for their estimation. Simulation results show that the proposed detection scheme achieves a detection performance comparable to unquantized systems and has a lower symbol error rate (SER) than the conventional quantized VB and linear minimum mean-squared error (LMMSE) methods. The effects of the azimuth range, and the antenna spacing and wavelength on the SER performance of all detection algorithms are also extensively analyzed.

关键词： Sigma-delta modulation Quantization (signal) Simulation Noise Symbols Massive MIMO Bayes methods Iterative methods detection algorithms Spatial resolution

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Fire detection System with YOLO and Flicker Analysis Using HSV Color Space Masking

Fire Detection System with YOLO and Flicker Analysis Using H...

引用

International Conference on ICT For Smart Society (ICISS)

作者： Calvin Figo Agil Alunjati Ulva Elviani Fadhil Hidayat School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

Early and reliable fire detection is crucial for minimizing fire damage and supporting firefighting efforts. Traditional sensor-based fire detection systems have limitations, prompting exploration into video analytics using computer vision techniques. This paper addresses the increasing fire incidents in Jakarta, Indonesia, underscoring the need for improved detection methods. We propose a video analytics-based fire detection system utilizing object detection with YOLO and flickering analysis with HSV color space. This approach aims to reduce false alarms by combining fire characteristics like flickering and color analysis, ultimately enhancing detection accuracy and contributing to improved fire safety measures. The proposed system achieved a 71.5% accuracy rate with 28.42 % fault detection rate, demonstrating proficiency in recognizing and differentiating real fire with static-fire objects, in order to reduce false alarms. Future development will focus on refining the fire detection algorithm to better capture the characteristics of moving fires and improving fire classification to reduce false positives, addressing the challenge of distinguishing real fires from highly realistic fire videos.

关键词： YOLO Accuracy Fluctuations Image color analysis Heuristic algorithms Fault detection Visual analytics Refining detection algorithms Fire safety

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A Lightweight Traffic Sign detection Algorithm Based on YOLOv5s

A Lightweight Traffic Sign Detection Algorithm Based on YOLO...

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Robotics, Artificial Intelligence and Intelligent Control (RAIIC), International Conference on

作者： Jiahao Sun Jifeng Liang Zhengda Liu School of Electronics and Information Engineering Wuxi University Wuxi Jiangsu China

ISBN: (数字)9798350352627

ISBN: (纸本)9798350352634

In the context of the rapidly developing autonomous driving technology, developing efficient and accurate traffic sign detection algorithms is of great significance. This paper proposes a lightweight algorithm based on YOLOv5s, aimed at enhancing detection performance and achieving parameter lightweighting. Firstly, by employing Ghost convolutions and Depthwise Convolutions (DW Conv) to construct a new backbone module, it successfully reduces the number of parameters and computational cost, thus improving the efficiency of the algorithm. Secondly, the introduction of the SPDC module enhances the algorithm’s capability of extracting information from multi-scale features, further improving the model’s detection accuracy. In addition, incorporating the CARAFE upsampling operator helps enhance the spatial resolution of the feature network, allowing the model to more accurately restore the details of the targets. The study focused on categories in the TT100K dataset with more than 200 instances shows that the improved model surpasses comparative methods in terms of average precision, overall performance, parameter quantity, and model size. This lightweight algorithm not only effectively enhances the accuracy of traffic sign detection but is also easy to deploy on embedded devices for real-time online detection, further advancing the intelligent level of environmental perception in autonomous driving.

关键词： YOLO Performance evaluation Accuracy Computational modeling Feature extraction Spatial resolution detection algorithms

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Mitigating Style Differences in Bitemporal Remote Sensing Images for Change detection

Mitigating Style Differences in Bitemporal Remote Sensing Im...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Tao Xie Lei Fu Jiayi Yang Qi Zang Dong Zhao Shuang Wang School of Artificial Intelligence Xidian University Shaanxi China Shaanxi Satellite Application Center for Natural Resources Shaanxi China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Change detection has seen significant advancements with the development of deep learning. However, due to variations in sensors or atmospheric conditions, bitemporal images often exhibit visually significant style differences, posing challenges for the detection of changed regions. This paper presents a change detection network designed to effectively address the challenges posed by style differences in bitemporal images. The proposed network comprises a color difference unification module and a generalized feature extraction module, which focuses the network on really changed areas. The color difference unification module harmonizes the color space of bitemporal remote sensing images, thereby mitigating the impact of style differences attributed to objective conditions. The generalized feature extraction module, ensuring robust feature representation for image pairs and further reducing style differences between bitemporal images. Experimental results demonstrate the superiority of our proposed method compared to existing change detection algorithms, confirming its suitability for fulfilling the requirements of change detection tasks.

关键词： Image sensors Deep learning Accuracy Image color analysis Feature extraction Sensors detection algorithms

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Development of Automated Neonatal Sleep-Wake Cycle Intelligent detection Algorithm

Development of Automated Neonatal Sleep-Wake Cycle Intellige...

引用

International Conference on Sensing Technology, ICST

作者： Jiaxi Wu Xin Wang Ke Zhang Weimin Huang Lina Men Shixiong Chen School of Medicine The Chinese University of Hong Kong Shenzhen Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China Department of Otolaryngology Shenzhen Children's Hospital Shenzhen China Department of Neonatology Shenzhen Children's Hospital Shenzhen China School of Medicine The Chinese University of Hong Kong Shenzhen

ISBN: (数字)9798350374827

ISBN: (纸本)9798350374834

While there are various algorithms for automatic identification of sleep-wake cycles based on electroencephalography (EEG), there is a notable lack of approaches utilizing amplitude-integrated electroencephalography (aEEG). As EEG is increasingly replaced by aEEG in many contexts due to its large data size and challenges in information capture, it is essential to advance sleep-wake cycle (SWC) detection algorithms based on aEEG. Previous research primarily established a threshold to differentiate between quiet sleep (QS) and non-QS, but this required manual adjustment of individual thresholds for each patient's data, leading to considerable inconvenience. Facing this challenge, the study developed an algorithm for the automatic identification of boundaries between QS and non-QS segments in the dataset. Each entry can have its own QS/non-QS boundary, eliminating the need for manual setting. The algorithm was developed under EEG recordings from seven neonates with post-menstrual ages ranging from 32 to 41 weeks. First, EEG data was converted into aEEG and subjected to smoothing. The upper and lower boundaries of the smoothed signal were utilized as the foundation for boundary assessment. The stable segment selection method was then applied to identify wide segments with limited variability, which were classified as QS periods. The analysis reveals that the detection algorithm performed well for extended QS periods (lasting over 15 minutes). In contrast, detection for shorter QS periods (less than 3 minutes) presented variability, with both false positives and false negatives observed. However, with proper adjustments, this method can effectively assist physicians in identifying suspicious QS phases and directly reviewing the corresponding video for validation.

关键词： Pediatrics Smoothing methods Sleep Manuals Medical services Electroencephalography Sensors Recording detection algorithms Monitoring

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An Improved Harmonic detection Method for Active Power Filter

An Improved Harmonic Detection Method for Active Power Filte...

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IEEE International Conference on Power and Energy Systems (ICPES)

作者： Tiantian Cao Zhengyang Ye Yufeng Dai Jiaxin Xu Xiaorong Wan Dayi Li State Key Laboratory of Advance Electromagnetic Engineering and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350391329

ISBN: (纸本)9798350391336

With the development of power systems, the harmonic problem has become more and more prominent. In the application of active power filters, the traditional ip-iq algorithm can only detect total harmonics and cannot obtain specific harmonics. To address this problem, this paper proposes an improved ip-iq algorithm for active power filters. This algorithm is improved based on the difference in separation objects of harmonic detection algorithms. It can convert specific harmonics into DC components. After low-pass filtering and inverse transformation, the specific harmonic components in the original three-phase current can be obtained. Compared with the traditional algorithm, mainly by modifying the transformation matrix $C_{x}$ and its inverse matrix ${C_{x}}^{-1}$ according to the difference between positive and negative sequence harmonics, it is simple to implement in active power filters and does not require complex calculations, greatly expanding the harmonic detection range and accurately detecting specified harmonics. A simulation model is constructed using Matlab/Simulink software to detect the 5th and 7th harmonics under two conditions of different harmonic contents in grid current. The results show that in the application of active power filters, when the content of specified harmonics is large, the detection result is more accurate. When the content is small, although there may be errors due to interference from other harmonics, it is within an acceptable range. Thus, the feasibility and effectiveness of the improved ip-iq algorithm in detecting specific harmonics in active power filters are verified.

关键词： Accuracy Interference Harmonic analysis Active filters Software Mathematical models Matrix converters MATLAB detection algorithms Power harmonic filters

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Loop detection Method Based on Multi-Frame Background Point Distance Histogram Encoding

Loop Detection Method Based on Multi-Frame Background Point ...

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Chinese Automation Congress (CAC)

作者： Yilong Deng Shiqian Wu Xiaokang Zhang Yasong Shi Caiqi Wang Yi Xiong School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Loop closure detection is a crucial component in Simultaneous Localization and Mapping (SLAM) for constructing high-precision maps, as it can eliminate cumulative errors generated during the operation of SLAM systems. To address the shortcomings of traditional loop closure detection methods in reverse loop closure detection, we propose a method based on multi-frame background points. Firstly, we segment the current point cloud into regions and identify the farthest point as the background point. Then, we encode the relative distance relationships of all background points into histograms. Finally, by comparing the scores of the current histogram with those of candidate histograms, as well as the scores from the previous five frames, we determine whether a loop closure has occurred. We conducted comparative experiments on the KITTI dataset, which is widely used in the autonomous driving field, against various loop closure detection algorithms. The results demonstrate that our method effectively identifies loop closures while ensuring real-time performance.

关键词： Point cloud compression Histograms Simultaneous localization and mapping Automation Robustness Real-time systems Encoding detection algorithms Autonomous vehicles

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