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检索条件"主题词=detection algorithms"

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Infrared Small Target detection Based on DETR Architecture and Super-Resolution Technique

Infrared Small Target Detection Based on DETR Architecture a...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Huanyu Yang Lijun Yang Jun Wang Yuming Bo Jiacun Wang College of Automation Nanjing Univ. of Sci. and Tech Nanjing China Dept. of Comp. Sci. and Softw. Eng. Monmouth University USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Infrared small target detection (ISTD) holds significant importance in domains such as maritime search and rescue, and autonomous driving. To enhance the detection capabilities of infrared small targets against complex backgrounds, a novel detection algorithm based on an improved detection Transformer (DTER) is proposed. This algorithm leverages the DTER detection framework and the EDSR network, utilizing super-resolution reconstructed images as inputs. It incorporates the Enhanced Multi-Scale Attention (EMA) module and an improved backbone structure. Moreover, it employs a micro-target detection encoder head with a new feature layer S2 to elevate the quality of minute feature extraction. The proposed method achieved a mAP@50 of 96% and mAP@(50:95) of 54.6% on a public dataset. Compared to current state-of-the-art methods for infrared small target detection, it demonstrates superior capabilities in reducing false positives and misses while maintaining commendable real-time performance.

关键词： Head Accuracy Superresolution Object detection Feature extraction Transformers Real-time systems detection algorithms Image reconstruction Remote sensing

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Apple Leaf Disease detection Algorithm Based on an Improved YOLOv8

Apple Leaf Disease Detection Algorithm Based on an Improved ...

引用

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

作者： Xin Zhao Qi Ye Mingtao Ma Lifen Wang Jilin Institute of Chemical Technology Jilin China JiLin Agricultural Science and Technology University Jilin China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

To improve the precision of recognizing apple leaf diseases in real-world applications, a detection model for apple leaf diseases is proposed, utilizing an enhanced YOLOv8 architecture. A Convolutional Block Attention Module (CBAM), incorporating both channel and spatial attention mechanisms, is introduced to enhance the extraction of disease-specific features while effectively ignoring irrelevant information in the images. A DyHead detection head replaces the original head in the YOLOv8 model, integrating scale, spatial, and task attention mechanisms to further enhance the model's detection performance. Compared with the original YOLOv8 algorithm, the improved YOLOv8 model increases mAP@0.5 by 1.6% and mAP@0.5:0.95 by 1.2%. Experimental results demonstrate that the proposed improved YOLOv8 algorithm achieves high recognition accuracy and detection speed, providing a precise real-time detection method for apple leaf diseases.

关键词： Head Attention mechanisms Accuracy Computational modeling Object detection Feature extraction Magnetic heads Real-time systems detection algorithms Diseases

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Automatic detection of Intellectual Disability Based on Functional Connectivity Using EEG

Automatic Detection of Intellectual Disability Based on Func...

引用

Intelligent Signal Processing and Effective Communication Technologies (INSPECT), IEEE International Conference on

作者： Nibras Hasan Zehra Fatima Sami Aas Mohammed Nausheen Ansari Omar Farooq Department of Electronics Engineering Aligarh Muslim University Aligarh India Centre for Biomedical Engineering Aligarh Muslim University Aligarh India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

Intellectual disability disorder (IDD) is a kind of neurodevelopmental disorder that adversely affects an individual's quality of life. Discriminating normal brain patterns from abnormal conditions is possible by identifying distinct connectivity patterns between groups. For this, Electroencephalography (EEG) is the most suitable choice in terms of the patient's ease and temporal characteristics. In this research, an automatic detection algorithm using EEG-based functional connectivity to distinguish between normal and IDD subjects IDD is proposed. This research utilized resting state EEG data gathered from 14 subjects (7 normal and 7 IDD). The methodology adopted includes alpha band extraction from the pre-processed EEG signals, followed by the computation of correlation coefficients for each non-overlapping 1-second window (epoch) to create a functional connectivity graph for both normal and IDD subjects. The connectivity features, so obtained, are then used to train multiple machine learning classifiers. After thorough evaluation, we selected the fine tree classifier due to its superior performance over other classification techniques. The generalized classification performance is obtained using Leave-One-Subject-Out Cross Validation, by leaving one subject out from each class during training. Notably, the achieved mean accuracy of 99.6% underscores the potential impact of this work in early diagnosis and personalized interventions for IDD.

关键词： Training Electrodes Accuracy Signal processing algorithms Signal processing Feature extraction Electroencephalography detection algorithms Random forests Optimization

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A Tea Buds detection Algorithm Based on Improved YOLO11

A Tea Buds Detection Algorithm Based on Improved YOLO11

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

作者： Zegang Niu Yulan Zhao Jilin Institute of Chemical Technology School of Information and Control Engineering Jilin China College of Electrical and Information Engineering Jilin Agricultural Science and Technology University Jilin China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

The detection and localization of tea buds are a critical part of the intelligent tea harvesting process, and using deep learning-based object detection technology to detect tea buds is of great significance for intelligent tea harvesting. This paper proposes an improved tea bud detection algorithm based on YOLO11, which uses the CBAM (Convolutional Block Attention Module) attention mechanism to automatically adjust the focus on different areas, enhancing the model's ability to capture targets against complex backgrounds and improving the model's accuracy. Experimental results on the tea bud dataset show that the improved algorithm has an average precision mAP50 that is 1.4% higher than the original YOLO11, and the mAP50:95 increased by 2.2%, and there is also a significant improvement in detection accuracy compared to other YOLO detection algorithms, providing a technical foundation for the application of intelligent tea bud harvesting equipment.

关键词： YOLO Location awareness Computer vision Accuracy Attention mechanisms Machine learning algorithms Computational modeling Image processing detection algorithms Optimization

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Community detection and Modularity Analysis in Stack Overflow Network Graphs

Community Detection and Modularity Analysis in Stack Overflo...

引用

Advanced Computing and Emerging Technologies (ACET), International Conference on

作者： Tabrej Ahamad Khan Imran Hussain Mohd Abdul Ahad Siddhartha Sankar Biswas Department of CSE Jamia Hamdard New Delhi India

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

Stack overflow is the largest online platform for learning and exchanging question answers for the computer programmers. On Stack Overflow, participants share topics that help programming learner, professionals, and experts. Using specific words or tags to describe a question makes it easier to explore the site. Tags are helpful in finding answers faster and connect with others who know about that topic. In this study we have chosen the stack overflow data set where in the first step exploratory data analysis is performed. Community detection algorithm is applied on 115 number of nodes and 490 number of links of the stack overflow dataset. In this paper, we use different community detection techniques and centrality metrics to analyses community detection in computer programming classes offered on Stack Overflow, a popular platform. To be more precise, we examine the community structure in the Stack Overflow courses using the Girvan-Newman, Louvain, Walk-trap, and Greedy modular community ***, we assess the modularity of the identified communities by utilizing the Girvan-Newman algorithm, which measures the degree of network segmentation into modules. Furthermore, we provide valuable insights into the organization and clustering of users in online learning platforms, facilitating the development of targeted interventions and community management strategies to enhance the learning experience.

关键词： Measurement Data analysis Heuristic algorithms Scalability Clustering algorithms Organizations Question answering (information retrieval) detection algorithms Programming profession

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Traffic Sign detection Algorithm Based on Improved YOLOv5

Traffic Sign Detection Algorithm Based on Improved YOLOv5

引用

International Conference on Advanced Computer Theory and Engineering, ICACTE

作者： Jingwen Sheng Donghui Shi Penglin Wang Xiaolan Gong School of Electronics and Information Engineering Anhui Jianzhu University Hefei China Anhui Province Key Laboratory of Intelligent Building & Building Energy Saving Hefei China

ISBN: (数字)9798331539887

ISBN: (纸本)9798331539894

Recently, driverless technology has developed rapidly, and improving the accuracy and speed of traffic sign detection is of great significance to reduce the frequency of traffic accidents and maintain the safety of people's lives and property. In order to solve the problem of low detection accuracy caused by small size, high density and complex background environment of traffic signs in actual production and life, an optimized YOLOv5s algorithm was proposed. First, SimAM was embedded in the Head part of YOLOv5s to adjust the attention weight, making the model more focused on important information and reducing the omission of small targets to a certain extent. Secondly, Focal GIoU was used to solve the problem of sample difficulty and class imbalance in target detection, so that the model could identify and locate the target more accurately. Finally, Mish was used as the activation function during model training, which improved the generalization ability and network optimization ability of the model. The experimental results show that the mAP.5 and mAP.5:.95 of the improved algorithm reach 93.8% and 87.8%, respectively. Compared with the base model, the improvement is increased by 3.4% and 3.5% respectively.

关键词： YOLO Training Accuracy Computational modeling Production Safety detection algorithms Optimization Accidents

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Based on the Application of Improved Yolov3 Intelligent detection Algorithm in Tank Truck Loading And Unloading Operations

Based on the Application of Improved Yolov3 Intelligent Dete...

引用

Communications and Information Technologies (GCCIT), Global Conference on

作者： Yaru Wang Xuesheng Li Jie Mou Qifu Bao Zhejiang Academy of Emergency Management Hangzhou China

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

The role of safety detection in the loading and unloading of tank trucks is very important, but there is a problem that safety detection is not intelligent enough. In the past, manual inspection could not solve the intelligent detection problem in the loading and unloading operation of tank trucks, and the detection was not standardized. Therefore, this paper proposes an improved YOLOv3 intelligent detection algorithm for intelligent detection and analysis. Firstly, the characteristic analysis theory is used to intelligently detect the tank truck, and the index is divided according to the intelligent detection requirements to reduce the safety operation in the interfering factor. Then, the characteristic analysis theory intelligently detects the tank truck, forms an intelligent detection scheme, and conducts the safety operation results Comprehensive analysis. MATLAB simulation shows that under the condition of certain intelligent detection standards, the YOLOv3 intelligent detection algorithm is improved for tank trucks Intelligent detection, safety operation accuracy, detection time are better than previous intelligent detection methods.

关键词： YOLO Loading Inspection Safety Information and communication technology detection algorithms MATLAB Standards Optimization Business

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Wheat-YOLO: A Real-Time and High Precision Object detection for Wheat

Wheat-YOLO: A Real-Time and High Precision Object Detection ...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Jin Xu Yue Sun Senyue Zhang Dongdong Sun Zhiyu Xiang School of Artificial Intelligence Shenyang Aerospace University Shenyang China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In the wheat detection work, the wheat is located in a complex environment, weeds and dried leaves will hinder the detection of wheat, the wheat images of shadows, wheat obscuring each other and other phenomena will lead to a reduction in the accuracy of the detection. At the same time, for the small object detection problem, most of the detection algorithms use the detection speed as a cost to improve the detection accuracy, and cannot do a good job of balancing between the two. To address the above issues, this paper proposes an improved YOLO detection algorithm, which aims to improve the detection accuracy of small targets in complex environments while minimising the cost of detection speed. Firstly, two attention mechanisms for small target detection are added to the backbone of YOLOv8; secondly, the target detection header with unified attention is used in the head part, which improves the expressive power of the detection header without any computational expense; and finally, the Inner-MPDIoU function, which is a combination of MPDIoU and Inner ideas, is used as the localisation loss in loss *** experiments on publicly available datasets show that the results of the network in this paper are improved in terms of both speed and accuracy compared to the original YOLOv8, enabling a better balance between detection accuracy and speed.

关键词： YOLO Accuracy Head Costs Attention mechanisms Real-time systems detection algorithms Cybernetics

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Distributed Belief Propagation detection for User-Centric Cell-Free Massive MIMO

Distributed Belief Propagation Detection for User-Centric Ce...

引用

International Symposium on Wireless Communication Systems (ISWCS)

作者： Kaining Yang Wenyue Zhou Xiaosi Tan Yongming Huang Chuan Zhang Lab of Efficient Architectures for Digital-Communication and Signal-Processing (LEADS) National Mobile Communications Research Laboratory Southeast University Nanjing China Purple Mountain Laboratories Nanjing China

ISBN: (数字)9798350362510

ISBN: (纸本)9798350362527

Benefiting from its near-optimal performance and hardware-friendly architecture, Belief Propagation (BP) detection has become an attractive approach for massive multiple-input multiple-output (MIMO) systems. However, its application in the recently emerged Cell-Free massive MIMO (CF-mMIMO) still remains unexplored. In response to this gap, this paper proposes a distributed BP detection algorithm tailored for user-centric CF-mMIMO systems. The proposed detector comprises two modules: 1) Module A for posterior messages updating at each access point (AP), and 2) Module B for computing a priori messages at the central processing unit (CPU). The CPU and APs iteratively exchange the updated messages until reaching the maximum number of iterations. Simulation results demonstrate that the proposed distributed BP detector outperforms the recent art by 7.0 dB SNR gains at 0.1 BER for a CF-mMIMO with 4 distributed APs, each equipped with 8 antennas to serve 8 single-antenna UEs in 16-QAM modulation.

关键词： Wireless communication Simulation Modulation Massive MIMO Detectors Central Processing Unit 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...

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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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