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检索条件"主题词=detection Algorithms"

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Pedestrian detection Algorithm Based on Multi-Scale Fusion and Dual-Backbone

Pedestrian Detection Algorithm Based on Multi-Scale Fusion a...

引用

Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Deshuai Bao Wenzhong Zhu School of Computer Science & Engineering Sichuan University of Science & Engineering Yibin China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Addressing the limitations of visible light singlemodal target detection algorithms, which are affected by external factors like illumination, and infrared single-modal algorithms, which lack complete target information, we propose a pedestrian detection algorithm based on dual-backbone and multi-modal multi-scale fusion, building upon *** network employs a dual-backbone structure derived from the YOLOv8n lightweight network. An SMMF(Scale-Multiple Multimodal Feature Fusion) module is designed to fuse features from both modalities at multiple scales. The GhostConv module is introduced to enrich features and reduce computational cost. Additionally, the SBAp2 is integrated into the network neck, enhancing the fusion of deep and shallow features and leveraging both deep semantic information and shallow image location information. The ShapeIoU is adopted as the loss function, replacing the original bounding box regression loss, to incorporate the shape and scale of the bounding box into the loss calculation, thereby improving target detection accuracy. Experimental results indicate that the improved algorithm achieves an AP0.5 of 78.5% and an FPS of 156.3. Compared to the original visible light single-modal and infrared single-modal YOLOv8n algorithms, the missed detection rates were reduced by 30.1% and 13.4%, respectively, while the AP0.5 is increased by 21.2% and 8.2%, demonstrating its superiority over the single-modal YOLOv8n detection algorithm.

关键词： Pedestrians Accuracy Shape Semantics Object detection Reconnaissance Real-time systems Neck detection algorithms Standards

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A Real-Time Video detection Algorithm for UAV Based on YOLOv8

A Real-Time Video Detection Algorithm for UAV Based on YOLOv...

引用

International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Xin Nie Qili Wu School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

Aiming at the common problem of small target detection in UAV real-time video surveillance, this paper proposes a UAV video detection algorithm based on improved YOLOv8 framework. By embedding a feature enhancement module specifically for small targets in the original YOLOv8 architecture, the algorithm effectively improves the recognition degree of the network for small targets. In order to avoid the excessive expansion of network parameters, the header network is simplified, the convolutional layer is removed and the number of channels is reduced to maintain the computational efficiency of the algorithm. Then this paper introduces the Channel Space Attention Mechanism module (CSAM), which makes the network pay more attention to the small target-dense region on the feature map by optimizing the weight distribution, thus enhancing the pertinence of target detection. In addition, a location-based label assignment strategy (LB-SimOTA) is proposed, which assigns different weights according to the intersection ratio (IoU) between the prediction frame and the real frame to optimize the overall quality of the network prediction frame. Experimental results on the VisDrone2019 dataset containing many small targets show that compared with the benchmark model YOLOv8-S, the proposed algorithm improves the accuracy of vehicle and pedestrian detection tasks by 8.44% and 8.12%, respectively, and achieves a frame rate of up to 90 FPS while maintaining the detection speed.

关键词： Accuracy Pedestrians Target recognition Object detection Streaming media Autonomous aerial vehicles Feature extraction Video surveillance Real-time systems detection algorithms

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An Improved SSD Deep Network-Based Algorithm for Eye State detection

An Improved SSD Deep Network-Based Algorithm for Eye State D...

引用

International Conference on Electronic Engineering and Informatics (EEI)

作者： Weikun Dai Jianbin Xiong Qi Wang Xiangjun Dong School of Automation Guangdong Polytechnic Normal University Guangzhou China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

Aiming at the problem of poor real-time performance in current eye state detection algorithms, a deep learning algorithm-improved single-shot multibox detector (SSD) with both real-time performance and detection accuracy is proposed. Firstly, the lightweight network MobileNetV2 replaces the original feature extraction network VGG16 to improve the detection speed. Then, spatial and channel squeeze-excitation network (scSENet) attention module is introduced to enhance the spatial features and channel features of the eye to improve the detection accuracy. The proposed method is tested on the public data set Closed Eyes in the Wild (CEW), achieving a mAP value of 96.12%, which is higher than current mainstream methods. The experimental results show that the algorithm has high accuracy and efficient detection speed in the task of eye state detection.

关键词： Deep learning Accuracy Detectors Feature extraction Real-time systems detection algorithms Informatics

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An Improved Object detection Algorithm Based on YOLOv10

An Improved Object Detection Algorithm Based on YOLOv10

引用

International Conference on Computer and Communications (ICCC)

作者： Wenhai Liu Song Xiao Lin Fu Shenyang Institute of Computing Technology University of Chinese Academy of Sciences Shenyang China School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Object detection is still an important research topic in computer vision. For years, YOLO series object detection framework has been the object of research and improvement by many researchers since its appearance. In this paper, the improved space-to-deepth downsampling convolution algorithm is used to replace the convolution downsampling algorithm of YOLOv10 aiming at the shortcoming that the downsampling convolution process in the original detection algorithm can not retain more spatial information. We introduced RepCSP module to improve C2f module in YOLOv10. By mixing up linear attention mechanism, the feature extraction module and linear attention mechanism are integrated, and the feature extraction ability of the model is improved. Combined with the improved convolutional downsampling network, the improved C 2fRepCSP module and the integrated linear attention mechanism, the model is trained and evaluated on VOC 2007 and VOC 2012 data sets, and the results show that the detection accuracy of the model has been improved.

关键词： YOLO Computer vision Attention mechanisms Accuracy Convolution Detectors Feature extraction Data models detection algorithms

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detection of False Data Injection Attacks in Cyber-Physical Systems

Detection of False Data Injection Attacks in Cyber-Physical ...

引用

IEEE International Symposium on Information Theory

作者： Souvik Das Avishek Ghosh Debasish Chatterjee Systems and Control Engineering Indian Institute of Technology Bombay India

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

This article addresses the problem of detecting, with high probability, the presence of a large class of history-dependent false data injection actuator attacks in cyber-physical systems (CPSs) modeled as stochastic linear time-invariant systems. Among the primary contributions is the introduction of a new detection algorithm or scheme that leverages the fundamental idea of separating or classifying two classes of state trajectories: The first class comprises state trajectories generated by the CPS under attack situations, while the second class includes state trajectories generated under nominal conditions. The existence and non-existence of such a separator are studied and these results are asymptotic. We then establish finite-time guarantees associated with the detection algorithm or scheme. A numerical example is provided to demonstrate the theory.

关键词： Actuators Particle separators Stochastic processes Cyber-physical systems Data models Trajectory detection algorithms

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Quickest detection in High-Dimensional Linear Regression Models via Implicit Regularization

Quickest Detection in High-Dimensional Linear Regression Mod...

引用

IEEE International Symposium on Information Theory

作者： Qunzhi Xu Yi Yu Yajun Mei School of Industrial and Systems Engineering Georgia Institute of Technology Department of Statistics University of Warwick Department of Biostatistics School of Global Public Health New York University

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

In this paper, we consider the quickest detection problem in high-dimensional streaming data, where the unknown regression coefficients might change at some unknown time. We propose a quickest detection algorithm based on the implicit regularization algorithm via gradient descent, and provide theoretical guarantees on the average run length to false alarm and detection delay. Numerical studies are conducted to validate the theoretical results.

关键词： Linear regression Numerical models Delays detection algorithms Information theory

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Automatic Pain detection Algorithm for Patients with Cancer Pain Using Wristwatch Wearable Devices

Automatic Pain Detection Algorithm for Patients with Cancer ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Hideyuki Hirayama Shiori Yoshida Konosuke Sasaki Emi Yuda Kento Masukawa Mamiko Sato Tomoo Ikari Akira Inoue Yoshihide Kawasaki Mitsunori Miyashita Department of Palliative Nursing Tohoku University Graduate School of Medicine Sendai Japan Department of Oncology Nursing Tohoku University Graduate School of Medicine Sendai Japan Tohoku University Graduate School of Information Sciences Sendai Japan Department of Palliative Medicine Tohoku University Graduate School of Medicine Sendai Japan Department of Urology Tohoku University Graduate School of Medicine Sendai Japan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Pain assessment becomes challenging for patients unable to self-report, given the subjective nature of pain. This study introduces an automatic pain detection model utilizing biological signals from wristwatch wearables and time series data from patients with cancer experiencing pain. Biological signals and pain data were obtained from 10 patients with cancer pain for 7 days during their hospitalization. A total of 73,154 minutes of data and 407 pain reports were obtained. We developed automatic classifiers to detect moderate or severe pain and pain above the personalized pain goal by several machine learning algorithms using per-patient and mixed data sets. The best-performing algorithm achieved an F1 score of 0.87, with enhanced performance using the personalized pain goal as the cutoff. While the generalized model requires improvement, the study demonstrates the feasibility of automatic pain detection using extended real-world patient data.

关键词： Performance evaluation Machine learning algorithms Pain Biological system modeling Time series analysis Data models Object recognition Wearable devices detection algorithms Cancer

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STOE-YOLOv5s: A Hard Hat Wearing detection Algorithm Based on Improved YOLOv5s

STOE-YOLOv5s: A Hard Hat Wearing Detection Algorithm Based o...

引用

Robotics and Computer Vision (ICRCV), International Conference on

作者： Tao Hu Wei Huang Jianghao Wei Qiang Liu Jiahuan Huang Jie Yu School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798331527426

ISBN: (纸本)9798331527433

To address the existing issues of missed detection and false detection of hard hats by mainstream detection models in complex environments, an improved hard hat detection algorithm, STOE-YOLOv5s based on YOLOv5s, has been proposed. Firstly, enhance the detection header structure, add multi-scale target detection layers, and improve the detection capability for small targets; Secondly, a BottleNeck Transformer (BoT3) module based on self-attention is added to the backbone network to enhance the model's ability to extract global features; Then, to solve the problem of positive and negative sample matching, a Simplified Optimal Transport Assignment (SimOT A) strategy is used to obtain the optimal matching result in the global range; Finally, an Efficient Multi-Scale Attention (EMA) mechanism is fused into the C3 module of the network neck to enhance the model's ability to capture features-rich areas. Experimental results on the hard hat dataset show that the mAP of the improved algorithm reaches 96.0%, and the recall rate reaches 91.8%. Compared with the original YOLOv5s algorithm, it has increased by 1.7 and 2.9 percentage points respectively, proving the effectiveness and advancement of the improved algorithm.

关键词： YOLO Head Accuracy Computational modeling Feature extraction Transformers Safety Resource management detection algorithms Optimal matching

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Research on the detection of Abnormal Cervical Cells Based on Improved YOLOv7

Research on the Detection of Abnormal Cervical Cells Based o...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Xuewei Wang Shuang Yuan Lidong Yang Dawei Niu Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing Inner Mongolia University of Science and Technology School of Digtial and Intelligence Industry Inner Mongolia China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Cervical cytologic screening is clinically important for the prevention and diagnosis of cervical cancer. Aiming at the many challenges in the detection of abnormal cervical cells, including the difficult detection of small targets of abnormal cervical cells, complex backgrounds, and uneven cell morphology, a detection algorithm for abnormal cervical cells based on improved YOLOv7 was proposed. The algorithm utilizes Deformable Convolutional Networks v2 (DCNv2) that adaptively de-tunes the scale and receptive field size to more effectively cope with the complexity and diversity of cervical cell images. The experimental results show that the improved YOLOv7 algorithm mAP achieves 57.26%, up to 73.88% accuracy, and 46.90% recall on the cervical abnormal cell dataset. Compared with the benchmark model, they increased by 4.07%, 3.39%, and 5.69%, respectively.

关键词： Accuracy Sensitivity Shape Prevention and mitigation Signal processing algorithms Signal processing Feature extraction Convolutional neural networks detection algorithms Cervical cancer

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Develop a liquid level detection algorithm for infusion bottles based on Ghost-YOLOv4

Develop a liquid level detection algorithm for infusion bott...

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Computer Information Science and Application Technology (CISAT), International Conference on

作者： Liang Jianfeng Thelma D. Palaoag Department of Computer Science University of the Cordilleras Baguio City Philippines College of Electronic and Information Engineering University of Beibu Gulf Qinzhou City China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

In view of the problems of weak anti-interference ability, low detection accuracy and high algorithm complexity that make it difficult to ensure real-time performance in current infusion bottle level detection methods, this paper proposes an infusion bottle level detection method based on the Ghost-YOLOv4 model. First, the collected infusion bottle level image sample data is enhanced to improve the versatility of the sample data, and the Labelimg tool is used to annotate the images and create data sets. Then, the lightweight Ghost main network structure is used to replace the CSPDarkenet53 main network structure of the original YOLOv4 model, thereby reducing the number of model parameters and improving the detection speed. Secondly, the regression box loss function of the Ghost-YOLOv4 model is optimized to reduce the impact of excessive linear calculations after replacing the main network, resulting in the loss of some feature information, on the detection accuracy, thereby improving the detection accuracy. Finally, the experimental results show that the improved Ghost-YOLOv4 model has about 85% fewer parameters than the original YOLOv4 model, and the detection speed is increased by about 115%. The Ghost-YOLOv4 model with optimized loss function has higher recognition frame positioning accuracy, which can provide better technical support for the automatic, high-quality and rapid detection of liquid level in infusion bottles.

关键词： Information science Accuracy Liquids Computational modeling Object detection Feature extraction Real-time systems Complexity theory detection algorithms

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