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检索条件"主题词=Detection Algorithms"

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High-precision 3D Facial Landmark detection with Curvature-fused Graph Attention Network

High-precision 3D Facial Landmark Detection with Curvature-f...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Juncheng Han Yuping Ye Di Wu Shiyang Long Zhan Song Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China School of Smart Marine Science and Engineering Fujian University of Technology Fuzhou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

With the rapid advancement of deep learning, 2D facial landmark detection algorithms have achieved satisfying results. However, due to the absence of large-scale accurately annotated 3D facial datasets, most current 3D facial landmark detection algorithms rely on 2D texture assistance or non-real digital 3D faces. The performance of these algorithms is limited by the accuracy of 2D texture mapping onto 3D faces and the difference between digital faces and actual faces. To tackle these challenges, we have built a large-scale, high-precision 3D facial database using a structured light system. Facial landmarks within the database are marked multiple times to ensure accuracy. Building upon this foundation, we proposed a novel point cloud sampling method and 3D facial landmark detection algorithm. This method utilizes a curvature-fused graph attention network (CGAT) to directly predict landmark coordinates from 3D point clouds. Initially, we extracted a subsampled point set carrying curvature information from the original 3D facial point cloud via geometric point sampling (GPS). Then, we used curvature-encoded positional information as the learning component of the attention module and employed it as a feature extractor to construct the CGAT. We assessed the performance of CGAT on two datasets, BU-3DFE and CIE-H3DF (Ours). Compared to the existing facial landmark detection algorithms, CGAT achieves higher accuracy.

关键词： Point cloud compression Three-dimensional displays Accuracy Databases Feature extraction Sampling methods Data mining detection algorithms Faces Global Positioning System

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Research and Application of Efficient detection Algorithm for Harmful Substances in Food

Research and Application of Efficient Detection Algorithm fo...

引用

Power, Electrical Engineering, Electronics and Control (PEEEC), International Conference on

作者： Hongyan Liu Yanchun Ruan Wei Wang Xinglin Zeng Ruichen Bu College of Cuisine Science and Technology Jiangsu Vocational College of Tourism Yangzhou Jiangsu China

ISBN: (数字)9798350378917

ISBN: (纸本)9798350378924

With food safety becoming the focus of global attention, rapid and accurate detection of harmful substances in food has become the key to protect public health. In this study, the efficient detection algorithm of harmful substances in food is deeply studied, aiming at developing a hybrid model combining advanced machine learning technology and big data analysis to realize rapid and accurate identification of harmful substances in food. By constructing a hybrid model (CNN-LSTM) based on Convolutional Neural Network (CNN) and Long Short Term Memory Network (LSTM), this study fully utilizes the complementary information in food image and spectral data to improve detection accuracy and efficiency. The experimental results show that the proposed CNN-LSTM hybrid model shows excellent performance in the detection of harmful substances in food, with high accuracy and strong generalization ability, and can be applied to various types of food samples and harmful substances. This study not only provides an efficient and convenient detection method for food safety supervision, but also provides a new idea and method for the research of harmful substances detection algorithm in food.

关键词： Electrical engineering Deep learning Accuracy Food safety Data models Convolutional neural networks Labeling detection algorithms Public healthcare Long short term memory

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Research on Helicopter Obstacle detection Algorithm Based on Object Recognition

Research on Helicopter Obstacle Detection Algorithm Based on...

引用

IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Jiang Li Mingze Ma Xiankuan Zhang Leilei Shi Unit 61255 of the People's Liberation Army Houma China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040

In this paper, a new 2D-Single Machine (2D-SM) algorithm is proposed, which is applied to helicopter obstacle detection based on target recognition to judge whether there are obstacles in the shutdown environment. The algorithm combines SSD (Single Shot MultiBox Detector) target detection algorithm, Support Vector Machine (SVM) classification algorithm and Dijkstra path search algorithm. SSD is used for multi-scale target detection, which can efficiently detect targets in the environment, generate accurate candidate boxes, and quickly identify obstacles in the shutdown environment. SVM classifies the detected obstacles to improve the accuracy of recognition. Dijkstra's algorithm is used to optimize path planning to ensure that the helicopter can land efficiently and safely while there are no obstacles in the shutdown environment. Finally, compared with the traditional detection algorithm, the results show that the 2D-SM algorithm can quickly identify obstacles in complex environments, provide a safe shutdown environment judgment for the helicopter, and ensure the safety of the helicopter during takeoff and landing. The algorithm has the characteristics of high efficiency and accuracy, and provides a new solution for helicopter obstacle detection.

关键词： Support vector machines Accuracy Target recognition Helicopters Object detection Detectors Path planning Classification algorithms Safety detection algorithms

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Power Quality Disturbance detection Method Based on Improved Adaptive S-Transform

Power Quality Disturbance Detection Method Based on Improved...

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Electrical Engineering and Control Science (IC2ECS), International Conference on

作者： Junming Wei Shixuan Lü Wei Wang Lijun Zheng Xin Liu Runze Hu Wenqi Wang Taiyuan University of Technology Taiyuan China State Grid Shanxi Electric Power Company Electric Power Science Research Institute Taiyuan China

ISBN: (数字)9798331510107

ISBN: (纸本)9798331510114

Aiming at the problems of poor real-time and low time-frequency resolution of the composite power quality disturbance detection algorithm, a power quality disturbance detection method based on improved adaptive S-transform (IAST) is proposed. A global adaptive Gaussian window is constructed as the kernel function of IAST, which can adaptively adjust the effective window length and spectrum of the window with the change of the detection frequency, avoiding the frequent switching of window parameters to improve time-frequency resolution and reducing algorithm complexity. Window parameters are selected with the objective of enhancing signal energy concentration, ensuring accurate time-frequency positioning of various disturbances. An automatic threshold method is used to determine the main frequency point of the actual disturbance signal and performing time-frequency transformation on these points, further improving the execution efficiency of algorithm. Simulation and measurement results show that compared with existing composite power quality disturbance detection algorithms has good real-time performance and time-frequency resolution capabilities, low computational complexity, and is suitable for real-time and accurate detection of complex power quality disturbances.

关键词： Time-frequency analysis Accuracy Power measurement Power quality Switches Real-time systems detection algorithms Kernel Signal resolution Tuning

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An Automatic Tooth Position and Dental Disease detection Algorithm Based on YOLOv8

An Automatic Tooth Position and Dental Disease Detection Alg...

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International Conference on Computer and Communications (ICCC)

作者： Hongliang Wang Ke Diao Lin Wu Xuerao Li Xin Zhou Xiaozhou Liu Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China School and Hospital of Stomatology China Medical University Liaoning Provincial Key Laboratory of Oral Diseases Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

With the development of digital dental imaging technology, panoramic tomographic images have become an effective tool for diagnosing dental diseases. However, due to the diversity and complexity of dental lesions, accurately identifying tooth positions and pathological conditions from these images remains challenging. To address the issue of low target detection accuracy caused by the high similarity and complex textures of teeth, this paper introduces YOLOv8-Teeth, an enhanced teeth detection algorithm designed for accurate identification of teeth positions and dental diseases from panoramic radiographs. Key innovations include integrating the C2f_ConvNeXtv2 module for improved feature extraction, employing the ASPP module for optimized multi-scale feature capture, and utilizing Focal-EloU Loss for refined bounding box regression. Experimental results on a custom dataset of 476 panoramic radiographs demonstrate significant performance gains, achieving a mean average precision (mAP) of 91.3%, precision of 93.6%, and recall of 88.7%. These improvements offer promising potential for early diagnosis and treatment planning in dental care.

关键词： Technological innovation Accuracy Computational modeling Teeth Tomography Feature extraction Real-time systems Dentistry detection algorithms Diseases

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Stochastic Gradient Descent Based MIMO detection with Interference Rejection Combining

Stochastic Gradient Descent Based MIMO Detection with Interf...

引用

International Conference on Communication Technology (ICCT)

作者： Yubin Zhu Ruobing Yang Ya Gao Kaining Han Jianhao Hu National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

Multiple-input multiple-output (MIMO) technology promises high spectrum utilization and system capacity and has been seen as a key technology for modern wireless communication systems. With the rapid increase in the demand for system capacity, the interference between adjacent cells cannot be ignored, and the antenna scale is constantly increasing, which makes it increasingly difficult for uplink MIMO receivers to achieve the cubic complexity of the MMSE-IRC algorithm. Some methods based on series or approximate solutions provide quadratic complexity but have difficulty achieving interference suppression. To solve this issue, we proposed a method for calculating the equalization matrix based on the stochastic gradient descent algorithm. We theoretically proved that it can converge to the equalization matrix of the MMSE-IRC algorithm. After that, we experimentally demonstrated that its performance loss is within 0.2dB and the complexity does not exceed the quadratic, which provides a potential solution for reducing the complexity of uplink receivers in massive MIMO systems.

关键词： Interference suppression Receiving antennas Massive MIMO Approximation algorithms Hardware Complexity theory Uplink detection algorithms

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Optimized and Improved YOLOv8 Dense Pedestrian detection Algorithm

Optimized and Improved YOLOv8 Dense Pedestrian Detection Alg...

引用

Communication, Image and Signal Processing (CCISP), International Conference on

作者： Jingkun Mao Haoyu Wang Deheng Li Electrical Engineering and automation Tianjin University of Technology Tianjing China Futu-re co. Nagoya Japan

ISBN: (数字)9798350356656

ISBN: (纸本)9798350356663

In large, densely populated scenarios, abnormal human gatherings are common, and due to the presence of many small-scale occluded pedestrians, missed detections are likely. To tackle this challenge, an improved multi-scale occluded pedestrian detection algorithm based on YOLOv8n is proposed. Firstly, to address the large variation in the scale of occluded pedestrians, the algorithm introduces the lightweight network RepViT to enhance multi-scale features. Next, the GAM attention module is added to improve the representation of multi-scale images and positional information. Finally, to improve the recognition precision for minor targets that are often missed, a small object detection head is introduced, using Inner-WIoUv3 as the loss function to enhance the model's convergence capability. Experimental results show that the improved model achieves enhancements on the public dense pedestrian datasets WiderPerson and CrowdHuman, with mAP50 increasing by 1.8% and 5.1%, and recall rates increasing by 2.2% and 5.5%, respectively. The improved algorithm shows a higher level of detection precision when compared with the baseline model.

关键词： Pedestrians Head Target recognition Signal processing algorithms Object detection Signal processing Feature extraction detection algorithms Convergence

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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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Asparagus detection Method Based on Attention Mechanism

Asparagus Detection Method Based on Attention Mechanism

引用

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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IVDETR: A Multi-Source Image Pig detection Method Based on Cross-Domain Feature Fusion

IVDETR: A Multi-Source Image Pig Detection Method Based on C...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Rui Song Hao Li Mengzhen Huang Zihan Huang Peng Li Xuwen He School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

In large-scale centralised pig farming, object detection algorithms can accurately track and monitor each pig. However, a significant proportion of current detection methods are based on visible images, and the colour differences between different pig breeds limit the applicability of the models. Furthermore, complex background interference, low illumination conditions, or high illumination intensity noise in visible scenes can likewise lead to reduced accuracy during detection. Therefore, we propose a cross-domain encoder based on the self-attention and design the IVDETR model, which integrates features from infrared and visible light images for pig object detection. In order to facilitate the training and evaluation of the model, we constructed a comprehensive, standardised infrared-visible light dataset for the first time. The experimental outcomes demonstrated that the detection accuracy was increased from 0.939 to 0.998 in a real pig breeding environment.

关键词： Training Accuracy Noise Lighting Object detection Feature extraction Pattern recognition detection algorithms Monitoring Farming

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