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检索条件"主题词=Detection Algorithms"

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Conformal Geometric Algebraic Description of Flight Collision detection Algorithm Based on Cylinder Protection Zone

Conformal Geometric Algebraic Description of Flight Collisio...

引用

Aerospace Engineering and Systems (ISAES), International Symposium on

作者： Haobo Wu Weidong Jiao Tianjin Key Laboratory of Intelligent Signal and Image Processing Civil Aviation University of China Tianjin China

ISBN: (数字)9798350350418

ISBN: (纸本)9798350350425

In order to provide sufficient reaction time for air traffic controllers and pilots, a Flight Conflict detection Algorithm (FCDA) based on Cylindrical Protection Zone (CPZ) in the Conformal Geometric Algebra (CGA) is proposed. Initially, the CGA description of the CPZ is represented by using the CGA forms of the central circle and central axis segments of the cylinder. Then based on the inner and outer product and geometric product operations of CGA, the positional relationship of the two CPZs is judged to determine whether a flight conflict occurs or not. Subsequently, we compared the performance of flight conflict detection methods based on rectangular protection zones, spherical protection zones, and cylindrical protection zones through experiments in CGA and Euclidean space, respectively, the experimental results show that in CGA, the proposed method has a faster speed than the flight conflict method based on cuboids, and is basically equivalent to the method based on spherical protection zones. Significantly, all CGA-based methodologies demonstrated superior speed over their Euclidean space counterparts. Considering the propensity of rectangular and spherical zones to elicit false alarms due to their expansive coverage of airspace, the CGA-CPZ-FCDA method emerges as the superior choice.

关键词： Algebra Computational efficiency Trajectory Complexity theory Logic Protection Spatial resolution detection algorithms Collision avoidance Aerospace engineering

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Pedestrian detection Algorithm Based on Improved YOLO v5

Pedestrian Detection Algorithm Based on Improved YOLO v5

引用

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

作者： Haoyang Tang Zhe Li Xi'an University of Posts and Telecommunications College of Automation Xi'an Shaanxi China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Aiming at the problems such as large scale change and poor detection accuracy, an improved YOLOv5 pedestrian detection algorithm based on drone view was ***, a multi-scale bidirectional feature network structure was proposed to enhance the multi-scale feature detection ability of YOLOv5 network, so as to adapt to pedestrian change detection at different ***, a location attention detection layer is established to suppress background interference. Finally, the occlusion screening module is created to screen out the occluded pedestrians and improve the detection accuracy of the *** order to verify the performance of the algorithm, a comparison experiment is carried out with the existing pedestrian detection algorithm. The experimental results show that compared with the original YOLOv5 algorithm, the algorithm's accuracy is improved by 10.01%, and compared with the YOLOv5-tiny pedestrian detection algorithm, the algorithm's accuracy is improved by 5.74%, which can detect pedestrians well in the perspective of drones.

关键词： YOLO Pedestrians Accuracy Change detection algorithms Signal processing algorithms Interference Signal processing Feature extraction detection algorithms Drones

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Validation of a Heuristic Intention detection Algorithm for a Powered Ankle Prosthesis Across Various Ambulation Tasks

Validation of a Heuristic Intention Detection Algorithm for ...

引用

IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Jeanne Evrard François Heremans Renaud Ronsse Institute of Mechanics Materials and Civil Engineering Louvain Bionics and Institute of Neurosciences UCLouvain Louvain-la-Neuve Belgium

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

In the recent past, considerable research efforts have focused on the development of powered lower-limb prostheses. As these devices strive to emulate biological joint behavior, their embedded controller plays a crucial role in adapting to the currently performed task. The challenge when developing such an algorithm lies in achieving high efficiency, characterized by accuracy and reactivity, while depending on a minimal number of sensors, preferably embedded inside the device. This paper introduces a high-level controller, in charge of detecting the user's intention, specifically designed for the Efficient and Lightweight Spring Ankle (ELSA) prosthesis. A heuristic method was preferred over machine learning classifiers for its simplicity. Embedded IMU and position sensor within the device provide direct inputs to the algorithm, which operates following straightforward rules. The efficacy of the algorithm is demonstrated through the identification of seven distinct locomotion tasks. Besides the intention detection, we also suggest what would be the most appropriate ambulation mode of ELSA for each task, implemented with smooth transitions. The algorithm was tested through a preclinical validation, involving a single transtibial amputee. Out of the 6 trials, 5 were considered as being consistent and allowed to identify design improvements. Importantly, the user provided positive feedback about the intuitiveness of the algorithm.

关键词： Ankle Performance evaluation Machine learning algorithms Machine learning Sensor phenomena and characterization Classification algorithms Springs detection algorithms Robots Prosthetics

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Cross-Modal Pedestrian detection Algorithm Based on Gradient Convolution Fusion

Cross-Modal Pedestrian Detection Algorithm Based on Gradient...

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Electronic Information Engineering and Computer Science (EIECS), 2021 International Conference on

作者： Yan Xia Guangqiu Chen Tongsen Zhang Yuhang Dai Changchun University of Science and Technology College of Electronic Information Engineering Changchun China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

Pedestrian detection is one of the important tasks in the field of computer vision. In recent years, the single-spectral pedestrian detection method widely used in visible light is easy to be affected by occlusion and illumination. In the night or dark light environment, it is easy to cause large deviation of detection results or miss detection. Firstly, the features of infrared and visible images are fused by using the Gradient Convolution Fusion Module (GCFM) designed in this paper, and then introduced into the improved YOLOv4 network for training and learning to achieve the purpose of pedestrian detection. In order to improve the detection accuracy, the attention mechanism is added to the YOLOv4 network. The proposed method is tested on the Kaist and MSRS datasets. The results show that the pedestrian detection algorithm proposed in this paper can improve the detection accuracy while ensuring real-time performance.

关键词： Training Computer vision Pedestrians Accuracy Attention mechanisms Convolution Lighting Feature extraction Real-time systems detection algorithms

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Research and Implementation of a Novel Dynamic K-Value K-Best detection Algorithm

Research and Implementation of a Novel Dynamic K-Value K-Bes...

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

作者： Xinwei Li Wei Chen Zhuhua Hu School of information and communication engineering Hainan University Haikou China

ISBN: (数字)9798350366143

ISBN: (纸本)9798350366150

In wireless communication technologies such as 5G and Wireless Local Area Networks (WLAN), multiple-input multiple-output (MIMO) technology plays a crucial role in enhancing spectral efficiency and increasing the throughput of communication systems. MIMO signal detection, as a key technology within MIMO systems, has been a major research focus in the quest for high-performance, low-complexity detection algorithms. The K-best detection algorithm, known for its high performance, offers superior results compared to traditional linear detection algorithms but also comes with increased complexity. To address this issue, this paper investigates and improves the K-best detection algorithm by proposing a dynamic K-value variable K-best detection algorithm. By gradually decreasing the K value layer by layer, the algorithm's complexity is significantly reduced. In a 4x4 antenna setup with 64QAM modulation, this approach reduces the computational load by approximately 46%, with a performance loss of only ***, the improved K-best algorithm is designed and optimized for hardware architecture. Compared with other literature, it consumes less resources and has higher throughput.

关键词： Wireless communication Wireless LAN Heuristic algorithms Modulation Computer architecture Throughput Hardware Computational efficiency detection algorithms MIMO communication

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Fire detection Algorithm Based on Improved YOLOv5s

Fire Detection Algorithm Based on Improved YOLOv5s

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

作者： Xiao Wang Shaopeng Li Peng Yan Xiao Ma Ruju Ni Yulu Wu Institute of Automation Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Aiming at the problem of low detection accuracy caused by existing image fire detection algorithms when performing model lightweighting, the paper proposes an improved fire detection algorithm for YOLOv5s. YOLOv5 compared other YOLO models, it has stronger adaptability and higher sensitivity in fire detection. Firstly, we introduce null convolution to improve the MobileNetV2 lightweight module and replace the CSPDarknet53 backbone network in YOLOv5s to ensure the detection accuracy and reduce the computational complexity of the fire detection model; secondly, we add the CA attention mechanism in front of the SPPF layer of the backbone network, which increases the attention of the model network to the features of spatial and channel information, and improves the positional relationship of the matching degree; finally, the fire image dataset is built to train the improved YOLOv5s algorithm model and complete the flame image detection. The experimental results show that the improved fire detection algorithm improves the detection accuracy while improving the model computation volume, and its model parameter count is reduced by 2.46M and the detection accuracy is improved by 3.1 %.

关键词： YOLO Adaptation models Solid modeling Accuracy Sensitivity Computational modeling Signal processing algorithms Fires Feature extraction detection algorithms

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Research on Lightweight Steel Rebar End Surface detection Algorithm Based on Improved YOLOv8

Research on Lightweight Steel Rebar End Surface Detection Al...

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International Conference on Artificial Intelligence and Computer Information Technology (AICIT)

作者： Qiming Zheng Xiang Jin Zeng Junyu Cai Hetao Ma School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798350365443

ISBN: (纸本)9798350365450

This paper proposes a steel bar detection algorithm model based on the YOLOv8 object detection algorithm. It improves the network structure by using EMA (Exponential Moving Average) attention to replace the original C2f module, and introduces the Dyhead detection head to achieve the unification of object detection head and attention, thereby enhancing the feature extraction capability of the base model. Experiments show that the improved method improves the mAP by 1.01 % compared with the original algorithm model, which can effectively improve the accuracy and speed of steel bar quantity detection, and is more in line with the needs of industrial automation.

关键词： Automation Accuracy Production Object detection Real-time systems Raw materials Production facilities Steel detection algorithms Bars

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SkyDataNet: An Object detection Algorithm with 2D Gaussian Loss for UAV-Based Aerial Images

SkyDataNet: An Object Detection Algorithm with 2D Gaussian L...

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IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)

作者： Mehmet Akif Ozkanoglu Ali C. Begen Sedat Ozer Ozer Lab. Bilkent University Ankara Turkiye Ozyegin University Istanbul Turkiye California State Polytechnic University Pomona Pomona USA

ISBN: (数字)9798350351422

ISBN: (纸本)9798350351439

In this paper, we introduce a novel object detection algorithm based on the center-point detection. In our architecture, we introduce using two HourGlass architecture as the backbone, and we introduce using a new module to unify the predictions made after each backbone. Furthermore, since bounding boxes are in varying aspect ratios, as opposed to using a scalar Gaussian variance, we introduce using 2D variance in the Gaussian loss function to predict center-points in our network. We present the performance of our proposed improvements on three aerial datasets by comparing them to center-point based detection algorithms.

关键词： Heating systems Object detection Information processing Prediction algorithms detection algorithms

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Asparagus detection Method Based on Attention Mechanism

Asparagus Detection Method Based on Attention Mechanism

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Computing, Communication, Perception and Quantum Technology (CCPQT), International Conference on

作者： Jie Yu Chao Zhang College of Information Science and Engineering Shandong Agricultural University Tai'an China

ISBN: (数字)9798331528386

ISBN: (纸本)9798331528393

Asparagus detection in greenhouse environments frequently encounters challenges due to complex backgrounds, leading to diminished accuracy. We proposes YOLOv10s-iRMB, an improved asparagus detection algorithm. Our method integrates an Inverted Residual Mobile Block (iRMB) into the YOLOv10s architecture. This design leverages depth-wise convolution to significantly reduce parameters. This improves feature extraction and overall accuracy. Residual connections within the module preserve information from earlier layers, promoting robust and stable training. Experimental results show that YOLOv10s-iRMB achieves a detection accuracy of 88.77%, marking a 9.69% improvement than YOLOv10s. Additionally, MAP@0.5:0.95 has also been significantly improved.

关键词： Training Accuracy Quantum computing Image edge detection Green products Manipulators Feature extraction Real-time systems Object recognition detection algorithms

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detection of Phytophagous Mite Induced Damage in Tea Plant by Image Processing and Deep Learning Techniques

Detection of Phytophagous Mite Induced Damage in Tea Plant b...

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International Artificial Intelligence and Data Processing Symposium (IDAP)

作者： Murat Tören Mustafa Akiner Bengisu Kaba Feyza Yazici Berkutay Asan Fatih Beriş Elektrik-Elektronik Mühendisliği Bölümü Recep Tayyip Erdoğan Üniversitesi Rize Türkiye Biyoloji Bölümü Recep Tayyip Erdoğan Üniversitesi Rize Türkiye

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

The objective of this study is to ascertain the structural variations resulting from the detrimental impact of invertebrates on tea plant leaves. To this end, image processing and deep learning techniques were employed to identify plot regions in Artvin, Rize and Trabzon, where tea cultivation is highly prevalent. The use of drones enabled the acquisition of images in these regions. The results of the examinations indicated the presence of tea pest mites in the region in question. As of recent years, studies were initiated to determine the damage observed in the region, following the commencement of image acquisition. The damage was identified with an accuracy rate of 98% through the utilisation of the YoloV8 algorithm, which represents a sophisticated image processing technology, in conjunction with the convolutional neural network deep learning method. This was achieved through the application of 70 training periods to 800 images. The Edge Step (ST) method was employed to ascertain the geometric differences in the tea leaves, while the Convolutional Neural Network (CNN) was utilised to achieve the minimum classification error. The images were captured using a UAV specifically designed for the study, as it offered a convenient solution even in challenging terrain. The images were subjected to RGB, HSV and black and white colour transformations through the application of image processing techniques. Subsequently, the images were resized and divided into two classes, namely healthy and diseased. The utilisation of these data sets, divided into two classes, facilitated the attainment of more expeditious and precise results through the deployment of the image detection algorithm YOLOv8I-seg. Consequently, the rate of damage propagation was also expressed as a percentage. The study has facilitated the early detection of this damage, which represents a significant threat to tea yield. This has enabled the presentation of valuable data sets that can inform strategies for

关键词： Deep learning Training Image color analysis Image edge detection Learning (artificial intelligence) Organisms Convolutional neural networks detection algorithms Drones Diseases

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