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Piktor-O-Bot: Integrated image processing algorithms for portrait drawing robot applications

Measurement: Sensors 2025年 38卷

作者： Hoang, Anh Tuan Csempesz, János Cserteg, Tamás Viharos, Zsolt János Budapest Hungary Faculty of Economics and Business John von Neumann University Kecskemét Hungary Doctoral School of Informatics ELTE Eötvös Loránd University BudapestH-1117 Hungary

Collaborative robots, or cobots, have emerged as vital assets in numerous industries, reshaping operational paradigms and fostering human-machine collaboration. This paper introduces Piktor-o-bot, a Universal Robots 5 series portrait drawing robot, which integrates advanced image processing algorithms with collaborative robotic functionalities to streamline and enhance portrait creation. By utilizing neural networks for human face detection, background removal, edge detection, semantic segmentation, clothes segmentation, and eye detection, Piktor-o-bot aims to achieve lifelike depictions efficiently. The proposed system integrates these specialized neural networks, innovative algorithms, and collaborative robotic capabilities to offer a promising and optimized solution. The individual components and their interactions contribute to the overall success of the Pictor-o-bot system, as demonstrated by its performance at several exhibitions. © 2024

关键词： Semantic Segmentation

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Bearing Fault image Recognition in Strong Noise Based on Optimized CNN Architecture

Bearing Fault Image Recognition in Strong Noise Based on Opt...

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image processing and Computer applications (ICIPCA), IEEE International Conference on

作者： Qiang Li Haowen Gai Kaijie Hu Northeastern University Shenyang China Tianjin College University of Science and Technology Beijing Tianjin China Zhonghuan Information College Tianjin University of Technology Tianjin China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

To address the problem of low accuracy and poor stability of bearing diagnostic models under strong background noise, a bearing fault image recognition method is proposed that reduces the randomness of the model by avoiding the need for artificial parameterization, which can introduce random factors. This method is based on hyperparameter optimization of the GoogLeNet convolutional neural network model and decision fusion. First of all, two-dimensional wavelet time-frequency variation of the original signal of the bearing vibration to construct an image dataset, one-dimensional classification is into a two-dimensional image problem; Secondly, three lightweight convolutional neural network architectures are selected for the noise immunity test, to get the best noise-resistant network architecture GoogLeNet; Finally, hyper-parameter optimization is performed for the network, comparing the mesh method, the progressive mesh method, and the group optimization algorithm, respectively, and getting The optimal parameters are then analyzed for decision fusion and visualization of the network. To verify the method proposed in this paper, we used the open Case Western Reserve University bearing dataset. The experimental verification demonstrates that the proposed method achieves a correctness rate of 94.33% in decision fusion under noise, with good accuracy and stability.

关键词： Accuracy image recognition Computer architecture Power system stability White noise Stability analysis Robustness Classification algorithms Convolutional neural networks Optimization

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Fuzzy Systems and Data Mining vii - Proceedings of FSDM 2021

Fuzzy Systems and Data Mining VII - Proceedings of FSDM 2021

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7th International Conference on Fuzzy Systems and Data Mining, FSDM 2021

ISBN: (纸本)9781643682143

The proceedings contain 52 papers. The topics discussed include: how artificial intelligence may impact your job;a deep neural network model for the prediction of major adverse cardiovascular event occurrences in patients with non-ST-elevation myocardial infarction;do reviews influence real estate marketing: the experience combing with natural language processing;ranking of trapezoidal bipolar fuzzy numbers based on a new improved score function;interpretable dual-feature recommender system using reviews;facial expression recognition and image description generation in Vietnamese;hierarchical digital control system performance;and generalized-multiquadric radial basis function neural networks (RBFNs) with variable shape parameters for function recovery.

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Apply Yolov4-Tiny on an FPGA-Based Accelerator of Convolutional neural Network for Object Detection 2

Apply Yolov4-Tiny on an FPGA-Based Accelerator of Convolutio...

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2022 2nd International Conference on artificial Intelligence and Industrial Technology applications, AIITA 2022

作者： Zhang, Fengxi Li, Yuying Ye, Zhihao School of Telecommunications Engineering Xidian University No.266 Xinglong Section of Xi Feng Rd Shaanxi Province Xi'an710126 China School of Artificial Intelligent Xidian University No.266 Xinglong Section of Xi Feng Rd Shaanxi Province Xi'an710126 China

With the continuous expansion of neural Network technology in the artificial intelligence field, for example, image recognition and retrieval, object detection, pixel processing, automatic speech generation, etc., Convolutional neural networks (CNN) and Deep Learning of neural networks (DNN) have made apparent breakthroughs. To improve the inference speed of images, the combination of FPGA-based acceleration and multiple model quantization methods has become one of the most contemporary alternative methods. This paper designed an FPGA-Based acceleration scheme combining software and hardware and effectively applied it to the Yolov4-Tiny object detection model, realizing the accelerated detection inference process from the original 6-7mins to 383ms. First, it chose the static quantization method of fixed-point numbers, fixed the position of the decimal point, and then added Batch Norm between the convolutional layer and the activation function to form a connection structure. Second, it further improved inference speed on an FPGA with a version of ZYNQ-7020 by increasing the bandwidth cap and reducing bandwidth requirements, employing a massive pipeline design. Finally, in the test of the Coco dataset, the plan has completed a substantial acceleration of the average inference speed of the Yolov4-Tiny object detection model from 7.13mins/Picture to 498.89ms/Picture, which has a high application value in the field of object detection. It dramatically improves the inference speed as well as keeps the average accuracy above 0.95. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

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Integrating the dynamic optimization strategies of Adam and AdaGrad

Integrating the dynamic optimization strategies of Adam and ...

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International Conference on Intelligent Control and Information processing (ICICIP)

作者： Zhixuan Huang Xiaonan Luo Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

artificial intelligence (AI) has been a key research area since the 1950s, initially focused on using logic and reasoning to create systems that understand language, control robots, and offer expert advice. With the rise of big data and deep learning, AI has advanced in applications like recommendation systems, image recognition, and machine translation, primarily through optimizing loss functions in deep neural networks to improve accuracy and reduce training *** descent is the core optimization method but faces challenges like slow convergence and local minima. To overcome these, algorithms like Momentum, AdaGrad, RMSProp, Adadelta, Adam, and Nadam have been developed, introducing momentum and adaptive learning rates to accelerate convergence. This paper presents a new optimization algorithm that combines the strengths of Adam and AdaGrad, offering better adaptability to different learning rates.

关键词： Training Heuristic algorithms Optimization methods Machine translation Logic artificial intelligence Robots Recommender systems Convergence Intelligent control

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artificial Intelligence Based Learning Approach for Leaf Disease Identification and Detection 2nd

Artificial Intelligence Based Learning Approach for Leaf Dis...

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2nd International Conference on Data Science, Machine Learning and applications, ICDSMLA 2020

作者： Karuna, G. Sahithi, K. Rupa, B. Amani, R. Swaraja, K. Meenakshi, K. Computer Science and Engineering GRIET Hyderabad India Electronics and Communications Engineering GRIET Hyderabad India

ISBN: (纸本)9789811636899

Plants and Crops get diseased due to many reasons. It might be because of diseases of stems, leaves, roots etc. This Paper mainly congregates on leaves. Leaf Disease identification and Detection has many applications for cultivators and farmers to know whether the plant is diseased or not. So that they can retort in dwarf time and decreasing the loss and then can obtain immense profits. This paper mainly focused at learning the disease of plant through leaves. Here, we scrutinize the leaf through image processing and extract features of particular leaf and then utilizing those features as a dataset and done preprocessing and then administering them in artificial Intelligence based learning algorithms like Convolutional neural networks to find disease. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning algorithms

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MCT-Net: a multi-branch hybrid CNN-transformer model for medical image segmentation

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Pattern Analysis and applications 2025年第2期28卷 1-21页

作者： Shen, Longfeng Diao, Liangjin Peng, Rui Chen, Jiacong Lu, Zhengtian Ge, Fangzhen Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB) College of Computer Science and Technology Huaibei Normal University Anhui Huaibei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Anhui Big-Data Research Center on University Management Huaibei Normal University Anhui Huaibei China

In recent years, neural networks have demonstrated substantial progress in medical image segmentation. However, accurately segmenting objects in medical images is often restricted by edge blurring, which complicates the delineation of structures of interest. The purpose of this study is to develop a multi-branch hybrid CNN-transformer model to address the issue of edge blurring in medical image segmentation. This approach enables more precise edge detection and segmentation. Integrating hybrid CNN and transformer decoders facilitates detailed recovery while preserving global structures, thus minimizing errors in regions with blurred edges. Additionally, we incorporate a hybrid attention mechanism at the model’s bottleneck to enhance feature transfer between the encoder and decoder. The model was trained using the ACDC, Synapse, and polyp datasets, with a focus on improving segmentation accuracy in regions with blurry edges. Experimental results reveal that the proposed model surpasses existing state-of-the-art methods on the ACDC, Synapse, and polyp datasets, demonstrating its superior performance and effectiveness. These results suggest that the proposed model offers a significant improvement in handling edge blurring, with potential applications in enhancing medical image analysis and diagnostics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Attention mechanism Convolutional neural network Edge information Medical image segmentation Transformer

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Collaborative Satellite Computing through Adaptive DNN Task Splitting and Offloading

Collaborative Satellite Computing through Adaptive DNN Task ...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Shifeng Peng Xuefeng Hou Zhishu Shen Qiushi Zheng Jiong Jin Atsushi Tagami Jingling Yuan School of Computer Science and Artificial Intelligence Wuhan University of Technology China School of Science Computing and Engineering Technologies Swinburne University of Technology Australia KDDI Research Inc. Japan

ISBN: (数字)9798350354232

ISBN: (纸本)9798350354249

Satellite computing has emerged as a promising technology for next-generation wireless networks. This innovative technology provides data processing capabilities, which facilitates the widespread implementation of artificial intelligence (AI)-based applications, especially for image processing tasks involving deep neural network (DNN). With the limited computing resources of an individual satellite, independently handling DNN tasks generated by diverse user equipments (UEs) becomes a significant challenge. One viable solution is dividing a DNN task into multiple subtasks and subsequently distributing them across multiple satellites for collaborative computing. However, it is challenging to partition DNN appropriately and allocate subtasks into suitable satellites while ensuring load balancing. To this end, we propose a collaborative satellite computing system designed to improve task processing efficiency in satellite networks. Based on this system, a workload-balanced adaptive task splitting scheme is developed to equitably distribute the workload of DNN slices for collaborative inference, consequently enhancing the utilization of satellite computing resources. Additionally, a self-adaptive task offloading scheme based on a genetic algorithm (GA) is introduced to determine optimal offloading decisions within dynamic network environments. The numerical results illustrate that our proposal can outperform comparable methods in terms of task completion rate, delay, and resource utilization.

关键词： Satellites Adaptive systems Wireless networks Collaboration artificial neural networks Delays Proposals Resource management Next generation networking Genetic algorithms

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Object Detection and Categorization in Complex Scenarios Using Deep Learning

Object Detection and Categorization in Complex Scenarios Usi...

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International Conference on Computer and applications (ICCA)

作者： Rayan Chowdhury Vikas Upadhyaya CSE NIIT University Neemrana India

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

images, which represent 2D depictions of 3D objects in our environment, are naturally decoded and categorized by the human neural network. In an effort to emulate this cognitive process, we employ machine learning algorithms, a subset of artificial intelligence focused on neural network training. This research work specifically explores the use of the YOLOv5 model in image identification and classification, aiming to enhance efficiency. Our investigation identifies key challenges such as intricate geometries, image resolution, real-time processing, environmental variations, and price considerations. Through phases involving data collection, model training, system integration, and assessment of performance, the research work successfully achieves proficient object categorization across diverse scenarios. These findings not only validate the approach's viability but also highlight areas for potential optimization and future exploration.

关键词： YOLO Training Computer vision Analytical models Three-dimensional displays Computational modeling neural networks System integration Data collection Data models

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An Energy-Efficient FPGA-based Convolutional neural Network Implementation 29

An Energy-Efficient FPGA-based Convolutional Neural Network ...

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29th IEEE Conference on Signal processing and Communications applications (SIU)

作者： Irmak, Hasan Alachiotis, Nikolaos Ziener, Daniel Univ Twente Comp Architecture Embedded Syst Enschede Netherlands Tech Univ Ilmenau Comp Architecture & Embedded Syst Ilmenau Germany

ISBN: (纸本)9781665436496

Convolutional neural networks (CNNs) are a very popular class of artificial neural networks. Current CNN models provide remarkable performance and accuracy in image processing applications. However, their computational complexity and memory requirements are discouraging for embedded real-time applications. This paper proposes a highly optimized CNN accelerator for FPGA platforms. The accelerator is designed as a LeNet CNN architecture focusing on minimizing resource usage and power consumption. Moreover, the proposed accelerator shows more than 2x higher throughput in comparison with other FPGA LeNet accelerators with reaching up 14 K images/sec. The proposed accelerator is implemented on the Nexys DDR 4 board and the power consumption is less than 700 mW which is 3x lower than the current LeNet architectures. Therefore, the proposed solution offers higher energy efficiency without sacrificing the throughput of the CNN.

关键词： CNN FPGA Accelerator LeNet

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