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检索条件"主题词=detection Algorithms"

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Traffic Sign Recognition Algorithm Based on Improved Real-Time detection Transformer

Traffic Sign Recognition Algorithm Based on Improved Real-Ti...

引用

Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Rui Zhang Minhan Zhang School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Accurate detection of traffic signs is of great significance in the fields of road safety, intelligent transportation, and autonomous driving. This paper proposes the MY-DETR traffic sign detection method, which incorporates three innovative modules: EDB-SOP, AIFI-HiLo, and Inner-IoU. The EDB-SOP enhances robustness to small targets, occlusion, and complex backgrounds through multi-branch feature extraction and spatial optimization pooling technology. The AIFI-HiLo module integrates low-frequency and high-frequency attention mechanisms, optimizing the processing of multi-scale and multi-frequency features, and improving detection accuracy and robustness. The Inner-IoU loss function focuses on the inner overlapping area of the bounding box, improving the accuracy of traditional IoU in cases of low overlap. Experimental results show that MY-DETR significantly outperforms some mainstream algorithms on multiple traffic sign datasets, especially in terms of m $A$ P50 and mAP50-95, providing a more efficient and accurate traffic sign detection solution for complex scenarios.

关键词： Accuracy Transportation Focusing Feature extraction Transformers Robustness Real-time systems Road safety detection algorithms Optimization

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FD-YOLOv8:An Improved Fire detection Model Base on YOLOv8n

FD-YOLOv8:An Improved Fire Detection Model Base on YOLOv8n

引用

Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Longxiang Li Zhe Liu Yangbo Ruan Beimin Chen School of Artificial Intelligence Jianghan University WuHan China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Fire detection technology plays a crucial role in the field of fire safety. However, traditional fire alarms driven by smoke detectors often fail to report fires in a timely and effective manner. To cope with these challenges, We proposed an improved fire detection algorithm FD-YOLOv8 based on YOLOv8n. In this study, we introduced the OCA positive traffic attention mechanism to optimize the Bottleneck layer, enhancing the model's accuracy in object detection. Additionally, we integrated GSConv, an efficient lightweight convolution, to design an improved C2f-OGBlock module, further enhancing the model's detection capability while reducing parameters and computational costs. Furthermore, we incorporated a highly efficient feature enhancement algorithm, HIFA, into the model, significantly improving its feature extraction capabilities. We compared the improved YOLOv8 model with existing models. Experimental results demonstrate that our improved model achieves superior detection performance, with mAP@50 increasing by 3.2% and mAP@50:90 improving by 1.2% compared to the original model.

关键词： Attention mechanisms Accuracy Computational modeling Object detection Visual systems Feature extraction Scattering parameters detection algorithms Smoke detectors Fire safety

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Research on Road Disease detection Algorithm Based on Improved YOLOv5s

Research on Road Disease Detection Algorithm Based on Improv...

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Electronics and Communication, Network and Computer Technology (ECNCT), International Conference on

作者： Xili Yang Hu Sheng Na Geng Wenqiang Li Unit: School of Computer and Communication Engineering Dalian Jiaotong University Dalian Liaoning China

ISBN: (数字)9798350366143

ISBN: (纸本)9798350366150

Addressing the issues of low detection efficiency and high subjectivity in traditional pavement disease detection methods, this study leverages deep learning algorithms in object detection to enhance detection accuracy while maintaining real-time responsiveness. We introduce a pavement disease detection algorithm, YOLOv5s-MBD, which enhances YOLOv5s. Initially, we replace the original CIoU Loss with MPDIoU Loss as the regression loss function, maximizing the DIoU value to improve the network's learning of the position and shape of bounding boxes. Subsequently, we integrate a BiFPN weighted bidirectional pyramid structure, enabling more comprehensive and efficient information propagation across multiple scales, thereby enhancing detection performance at different scales. Lastly, we introduce a decoupled head into the detection head of YOLOv5s to better utilize semantic information between low-level and high-level features, thus enhancing detection accuracy and network convergence speed. The results of the experiment demonstrate that the YOLOv5s-MBD algorithm has improved the mAP @ 0.5 for disease recognition by 5.6%, and 1.6% respectively compared to YOLOv3, YOLOv4, YOLOv5s, and YOLOv8 on the GRDDC2020 dataset, and the recognition accuracy and recall rate have also been improved to varying degrees.

关键词： YOLO Accuracy Head Shape Roads Semantics Real-time systems Neck detection algorithms Diseases

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Research on AI-Based Target detection Technology for Sports Images

Research on AI-Based Target Detection Technology for Sports ...

引用

Electronics and Devices, Computational Science (ICEDCS), International Conference on

作者： Shujian Zhang Department of Physical Education College of General Education Guangdong University of Science and Technology Dongguan China

ISBN: (数字)9798331527624

ISBN: (纸本)9798331527631

With the rapid development of artificial intelligence, deep learning-based target detection technology has been widely applied in various fields. Sports images, as a special application scenario, present new challenges for target detection due to their complex actions and rapidly changing targets. This paper aims to study AI-based target detection technology for sports images by analyzing and improving existing detection algorithms and proposing an optimized detection algorithm suitable for sports scenarios. First, the paper outlines the basic theories of AI and target detection technology, then analyzes the characteristics of sports images and the current application status of detection algorithms, and finally designs and implements a target detection system for sports images. Experiments validate the proposed algorithm’s effectiveness and superiority in various sports scenarios. This research provides a theoretical foundation and technical support for applications such as sports analysis, intelligent refereeing, and athlete training.

关键词： YOLO Training Performance evaluation Scientific computing Neural networks Lighting Reliability detection algorithms Artificial intelligence Sports

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Open Vocabulary SAR Object detection with Denoising and Scaled Box Loss

Open Vocabulary SAR Object Detection with Denoising and Scal...

引用

Chinese Automation Congress (CAC)

作者： Yuhao Jin Qiujin Liang Tao Zhang Jiaqi Yu Department of Automation Tsinghua University Beijing China Beijing Institute of Control and Electronic Technology

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Synthetic aperture radar (SAR) has important applications in military, geology and other fields due to its high resolution and all-day, all-weather operation. However, the inherent speckle noise of SAR images seriously restricts the effectiveness of detection algorithms. To address this challenge, this paper proposes an open vocabulary SAR object detection model that combines a denoising network and an open vocabulary detection model, named Denoise GroundingDINO (DGD). We add a denoising network AGSDNet before image encoder to remove speckle noise in SAR images and propose a new box loss function called Scaled SCA (SSCA) Loss to improve the model accuracy. Experimental results show that the DGD model has achieved excellent performance on the SSDD, HRSID and SARDet-lOOK (part) datasets, verifying the superior ability of the DGD model in the field of SAR object detection.

关键词： Vocabulary Image resolution Geology Noise reduction Noise Object detection Speckle Radar polarimetry detection algorithms Synthetic aperture radar

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Bioacoustics for North Atlantic Right Whale detection Based on Deep Learning

Bioacoustics for North Atlantic Right Whale Detection Based ...

引用

OCEANS

作者： Marc-André Blais Moulay A. Akhloufi Dept. of Computer Science Perception Robotics and Intelligent Machines Lab (PRIME) Université de Moncton Moncton NB Canada

ISBN: (数字)9798331540081

ISBN: (纸本)9798331540098

Climate change, pollution, shipping lanes and other man-made factors are significantly impacting the biodiversity of our planet. Whales represent some of the most endangered animals of our time with some species, such as the North Atlantic right whales, have fewer than 400 individuals left. These large mammals play a crucial role in maintaining the health of our planet due to their various attributes, such as their carbon sequestration abilities. Detecting, classifying and tracking these whales can provide us with tools for their management. Passive acoustic monitoring allows researchers to identify whales through audio recordings based on their unique sounds. We propose detecting North Atlantic right whales using passive acoustic monitoring audio clips. Our data consists of whale upcalls collected in the Gulf of St. Lawrence and the Gulf of Maine. In our work, we compare various audio visualization transformations such as the wavelet transformation, Short-Time Fourier Transform and a custom algorithm. Using the visualized audio, our goal is to use various deep learning algorithms to detect whales. We employed multiple pre-trained convolutional neural network algorithms, including DenseNetl21, EfficientNetB7 and Xception. the wavelet transformation achieved the best overall results with a validation accuracy of 90% on most detection algorithms. Specifically, DenseNet169 combined with the wavelet transformation achieved the highest accuracy of 90.39%. Using ensemble learning on the best performing algorithms, we increased the accuracy of our approach to 91.86 %. Overall, our results demonstrate the strong potential of using audio visualization with deep learning algorithms to detect North Atlantic right whales. Further work in this field, such as exploring more algorithms, could further enhance the effectiveness of our approach.

关键词： Wavelet transforms Deep learning Accuracy Planets Whales Data visualization Ensemble learning detection algorithms Carbon sequestration Monitoring

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Research on road damage detection based on improved YOLOv8 in road scene

Research on road damage detection based on improved YOLOv8 i...

引用

Chinese Control and Decision Conference, CCDC

作者： Chen Zhou Zhongjun Xiao School of Electrical Engineering and Automation Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Road damage detection plays an important role in driving safety and road condition repair. Aiming at the existing inefficient and complex algorithms and methods, this paper provides an improved YOLOv8 detection algorithm: YOLOV8-MS(MPCA and Slim-neck), which combines precision and lightweight. Firstly, the Slim-neck structure is used in the head structure to reduce the computation. Secondly, the Multipath Coordinate Attention (MPCA) mechanism is added in the backbone structure, which increases the receptive field, improves the feature extraction ability of road defects, and reduces the interference of irrelevant features. Finally, we compare the improved YOLOv8 algorithm with other existing algorithms to verify the effectiveness of our proposed improvements. The experimental results show that in RDD2022 data set and self-built data set, compared with the original algorithm, the improved algorithm improves by 2.7% and 1.7% respectively with mAP50, and 1.9% and 0.6% respectively with MAP50-95. The computational complexity is reduced by 9.88%, which realizes the purpose of improving algorithm accuracy and lightweight.

关键词： Accuracy Roads Interference Maintenance engineering Feature extraction Safety detection algorithms

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GNSS Time Synchronization Attack detection Algorithm Based on Clock Bias Change Covariance

GNSS Time Synchronization Attack Detection Algorithm Based o...

引用

Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Peng Jia Qingmin Liao Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

This paper proposes a GNSS Time Synchronization Attack (TSA) detection algorithm based on clock bias change covariance. By leveraging the synchronization characteristics in the variation patterns of TSA signals, the algorithm effectively detects TSA by calculating the covariance of clock bias changes. Experimental results demonstrate that, compared to the clock drift correlation algorithm, this algorithm not only achieves higher detection sensitivity and accuracy, enabling a clear distinction between normal and TSA signals, but also operates faster, detecting TSA 17.86% earlier on average. Additionally, it reduces computational complexity, saving 17.86% of processing time. This practical detection algorithm is particularly suitable for resource-constrained hardware devices, enhancing the GNSS system's resistance to TSA, ensuring timing accuracy and system stability, and providing reliable security for critical infrastructure.

关键词： Global navigation satellite system Machine learning algorithms Accuracy Sensitivity Heuristic algorithms Hardware Critical infrastructure Synchronization detection algorithms Clocks

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Improved Traffic Sign detection Algorithm Based on Lightweight YOLOv8n

Improved Traffic Sign Detection Algorithm Based on Lightweig...

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Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Qiang Li Tengfei Yang School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou China

ISBN: (数字)9798331522285

ISBN: (纸本)9798331522292

Traffic sign detection constitutes a vital element in the environmental perception capabilities of autonomous driving systems. Aiming at the problem of reduced detection accuracy for small traffic signs and excessive model complexity in complex environments, an improved algorithm for traffic sign detection based on YOLOv8n is proposed. Specifically, the Squeeze Aggregated Excitation (SaE) module is integrated into C2f to optimize the backbone structure, enabling collaborative enhancement of local details and global semantics and significantly improving the detection of small targets. Then, a lightweight CNN-based Cross-scale Feature Fusion (CCFF) mechanism is adopted, effectively reducing the number of model parameters while enhancing robustness and adaptability to scale variations. In addition, the Weighted Intersection over Union (WIoU) loss function enhances the model's target localization accuracy by assigning adaptive weights to different regions. The experimental results show that the proposed algorithm improves average precision by 14.3% and the parameters decreased by 7.3% on the TT100K dataset. These enhancements not only streamline the model architecture but also enhance detection performance significantly.

关键词： Seminars Adaptation models Accuracy Rain Snow Semantics Robustness Complexity theory Scattering parameters detection algorithms

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Northeast cold vortex detection algorithm based on reanalysis data prediction

Northeast cold vortex detection algorithm based on reanalysi...

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Computer Applications and Information Technology (ISCAIT), International Symposium on

作者： Xi Jing Computer and Information Engineering college TianJin Normal University Tianjin China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

The Northeast Cold Vortex (NECV) is a significant atmospheric phenomenon characterized by a large-scale, high-altitude cold low-pressure system that predominantly influences the northeastern region of China and its adjacent areas. This persistent weather system typically maintains its activity for periods ranging from 3 to 4 days or longer, exhibiting substantial meteorological impacts. Current methodologies for determining the spatial extent of the NECV primarily rely on extensive data analysis, which involves substantial computational resources and prolonged data processing time. Based on the above reasons, this paper proposes a method to detect the northeast cold vortex by forecasting and reanalyzing data and combining it with satellite cloud images, which solves the problem of poor timeliness of detecting the northeast cold vortex by using ERA5, and improves the detection capability of the northeast cold vortex. Compared with the single use of satellite cloud image to detect the northeast cold vortex, its effect is significantly improved, and it has guiding significance for the detection methods of the northeast cold vortex.

关键词： Satellites Data analysis Clouds Prediction algorithms Distance measurement detection algorithms Information technology Forecasting Optimization Meteorology

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