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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VIII"

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Intelligent patrol wheeled robot based on image recognition 3

Intelligent patrol wheeled robot based on image recognition

引用

3rd International conference on machine vision, Automatic Identification, and Detection, MVAID 2024

作者： Wu, Wei Lv, Xinyue Pei, Zeyu An, Changying Ning, Jixiang Gao, Zhenzhen College of Engineering China University of Petroleum-Beijing at Karamay Karamay834000 China

ISBN: (纸本)9781510681880

In recent years, with the development of artificial intelligence technology, intelligent robots are more and more widely used in many fields. In this paper, an intelligent patrol wheeled robot based on image recognition is designed and implemented. The robot utilizes Raspberry PI as its core control unit and integrates sensing equipment such as cameras and ultrasonic sensors. The functions of autonomous navigation, intelligent obstacle avoidance and path tracking are improved through computer vision and machine learning technology. The main technologies include OpenCV for image processing, HOG feature extraction and SVM algorithm for traffic sign recognition, and traffic light detection system based on color recognition. In addition, this paper also discusses the use of positional PID control algorithm to achieve visual lane keeping scheme. Through detailed system design and experimental verification, this paper shows the performance benefits of the intelligent robot in practical applications, and provides a new idea for the future research of intelligent robots. It is expected that the robot has a wide range of application potential in intelligent transportation, public safety and other fields. © 2024 SPIE.

关键词： Intelligent robots

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From Templates to Transformers: A survey of Multimodal image Captioning Decoders

From Templates to Transformers: A survey of Multimodal Image...

引用

2023 International conference on Computer, Electronics and Electrical Engineering and their applications, IC2E3 2023

作者： Sharma, Himanshu Padha, Devanand Central University of Jammu Department of Computer Science and It Rahya-Suchani Jammu India

ISBN: (纸本)9798350338003

Automated text-to-image (image synthesis) and image-to-text (image captioning) generation are two of the most challenging and cutting-edge fields of study in Computer vision (CV) in conjunction with Natural Language processing (NLP). The image-to-text synthesis, also known as image Captioning (IC), has numerous applications in visual assistance, machine vision, remote security, healthcare, and remote sensing, among others. Since their origin, the IC frameworks have been constructed utilizing a two-subcomponent pipeline consisting of the visual feature extraction and natural language modeling subcomponents. The recent surge of interest in the applications of sequential deep learning models in machine translation has resulted in the development of efficient language modeling architectures. Deep sequential modeling architectures, such as Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Units (GRU), tackle the intricacies of the multi-modular space far better than existing machine learning-based translation approaches. The tremendous growth of deep language modeling architectures in IC necessitates a comprehensive review of its literature. In this survey, we conduct an exhaustive and analytical analysis of the language modeling architectures used in IC frameworks for caption generation, along with their training corpus datasets. We also identify a list of open research issues and potential research areas for future work. © 2023 IEEE.

关键词： Natural language processing systems

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Performance Improvement in Welding Operations Through image processing 2

Performance Improvement in Welding Operations Through Image ...

引用

2nd International conference on Artificial Intelligence and machine Learning applications, AIMLA 2024

作者： Sharma, Mohit Kumar Menon, Soumya V Tripathy, Padmaja Vivekananda Global University Department of Electrical Engineering Jaipur India School of Sciences Department of Chemistry and Biochemistry Karnataka Bangalore India ARKA JAIN University Department of Mechnical Engineering Jharkhand Jamshedpur India

ISBN: (纸本)9798350349221

This examination intends to enhance the overall performance of welding operations through picture processing. It's going to use an aggregate of PC vision and gadgets, getting to know to perceive better and tune welds, improve the accuracy of the system, and reduce the capability for mistakes. Particularly, the study will make use of a deep learning method to classify welds in specific classes, allowing the welding operations to be more effectively monitored and operated. Additionally, a convolutional neural network technique will be utilized to pick out the welds and estimate the vital parameters from the image statistics. Sooner or later, a robot arm geared up with a digital camera and a torch can be used to validate the welding process in an actual-world scenario. The effects of this take a look at will be used to enhance the performance and nice of welding operations through higher visibility into the system. © 2024 IEEE.

关键词： Welds

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A Comparative Study on Pruning Deep Convolutional Neural Networks Using Clustering Methods: K-Means, CLIQUE, DENCLUE, and OptiGrid 24

A Comparative Study on Pruning Deep Convolutional Neural Net...

引用

9th International conference on Multimedia and image processing (ICMIP)

作者： Alqemlas, Danah Saud Jeragh, Mohammad Esmaeel Kuwait Univ Kuwait Kuwait Kuwait Oil Co Kuwait Kuwait

ISBN: (纸本)9798400716164

In the past years, machine learning (ML) and deep learning (DL) have led to the advancement of several applications, including computer vision, natural language processing, and audio processing. These complex tasks require large models, which is a challenge to deploy in devices with limited resources. These resource-constrained devices have limited computation power and memory. Hence, the neural networks must be optimized through network acceleration and compression techniques. This paper proposes a novel method to compress and accelerate neural networks from a small set of spatial convolution kernels. Firstly, a novel pruning algorithm is proposed based on the density-based clustering method that identifies and removes redundancy in CNNs while maintaining the accuracy and throughput tradeoff. Secondly, a novel pruning algorithm based on the grid-based clustering method is proposed to identify and remove redundancy in CNNs. The performance of the three pruning algorithms (density-based, grid-based, and partitional-based clustering algorithms) is evaluated against each other. The experiments were conducted using the deep CNN compression technique on the VGG-16 and ResNet models to achieve higher accuracy on image classification than the original model at a higher compression ratio and speedup.

关键词： Neural Network Pruning Clustering Methods image processing

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Research and Implementation of Material image Subject Segmentation Method Based on machine vision 5

Research and Implementation of Material Image Subject Segmen...

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5th International conference on Advances in Electrical Engineering and Computer applications, AEECA 2024

作者： Wang, Lu Qingdao University of Science and Technology Chinesisch-Deutsche Technische Fakultat Qingdao China

ISBN: (纸本)9798350355253

This article proposes a simple and effective method for image subject segmentation. Our research mainly focuses on the characteristics of material images in the experimental platform. Through in-depth research, we have found that using edge detection, morphological operations, and fill truncation can form an efficient subject segmentation technique. To verify the effectiveness of our proposed method, we conducted a series of experiments. The results indicate that our method only takes 0.238 seconds to process a single image. This speed is very impressive and demonstrates the potential application of this method on embedded devices. Our method is very suitable for embedded devices such as experimental benches. Embedded devices often have limited computing power and resources, thus requiring an efficient and lightweight subject segmentation method. Our proposed method precisely meets these requirements because it is simple and effective, and can quickly perform image subject segmentation in resource limited environments. In summary, our method has significant advantages in image subject segmentation problems. It is not only simple and easy to understand, but also efficient and fast, making it very suitable for use on embedded devices such as experimental benches. We believe that this method will have a positive impact on the field of image processing and bring more possibilities for related application areas. © 2024 IEEE.

关键词： machine vision

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Automatic detection and recognition of humans with YOLO-based deep learning using MuSES-generated EO/IR synthetic imagery 2

Automatic detection and recognition of humans with YOLO-base...

引用

conference on Synthetic Data for Artificial Intelligence and machine Learning - Tools, Techniques, and applications II

作者： Klein, Mark D. Edel, Zachary J. Packard, Corey D. Hendrickson, Jacob N. Levanen, Audrey Rynes, Peter L. ThermoAnalytics Inc 23440 Airpk Blvd Calumet MI 49913 USA

ISBN: (纸本)9781510673892;9781510673885

machine learning algorithms are capable of processing image-based scenes, detecting and recognizing embedded targets. This has been demonstrated by data scientists and computer vision engineers, but performant algorithms must be robustly trained to successfully complete such a complex task. This typically requires a large set of training data on which the algorithm can base statistical predictions. Electro-optical infrared (EO/IR) remote sensing applications necessitate a substantial image database with suitable variation for adept learning to occur. For human detection/recognition applications diversity in clothing ensembles, pose, season, times of day, sensor platform perspectives, scene backgrounds and weather conditions can be included in training image sets to ensure sufficient input variety. However, acquiring such a diverse image set from measured sources can be a challenge, especially in thermal infrared wavebands (e.g., MWIR and LWIR). Alternatively, generating synthetic imagery with appropriate features is possible and has been shown to perform well, but a careful methodology must be followed if robust training is to be accomplished. In this work, MuSES and CoTherm are used to generate synthetic EO/IR remote sensing imagery of various human dismounts with a range of clothing, poses and environmental factors. The performance of a YOLO ("you only look once") deep learning algorithm is studied, and sensitivity conclusions are discussed.

关键词： EO/IR scene simulation machine learning artificial intelligence target signature automated image generation signature prediction deep learning EO/IR signature prediction

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image Feature Extraction and Tracking for Robot vision Servo Control System Based on the Transformer Model

Image Feature Extraction and Tracking for Robot Vision Servo...

引用

2024 International conference on Telecommunications and Power Electronics, TELEPE 2024

作者： He, Yanqiu Zhu, Yanyan Liu, Haisheng Harbin Institute of Petroleum Heilongjiang Harbin150028 China Harbin Huade University Heilongjiang Harbin150025 China The Seventh School of Harbin New District Heilongjiang Harbin150080 China

ISBN: (纸本)9798350369212

Robot vision servo control systems play an important role in modern automation systems, and image feature extraction and tracking, as its key components, have a direct impact on its performance and application scope. In this paper, we explore a novel approach based on the Transformer model, aiming to improve the image feature extraction and tracking function in robot vision servo control systems. First, we briefly introduce the basic principles of the Transformer model and its successful applications in the field of natural language processing. Then, we discuss in detail how to apply the Transformer model to image feature extraction and evaluate its performance with experimental results. Subsequently, we further discussed how to realize the image tracking function by using the Transformer model and proposed a new framework of visual servo control system for robots. Finally, we summarize the research results and look forward to possible future research directions. This research provides new ideas and methods to improve the performance and application range of robot visual servo control systems. © 2024 IEEE.

关键词： machine vision

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Hardware Implementation of machine vision System for Component Detection 7th

Hardware Implementation of Machine Vision System for Compone...

引用

7th International conference on Emerging Research in Computing, Information, Communication and applications, ERCICA 2022

作者： Smruthi, P. Prajna, K.B. John, Jibin G. Pasha, Aslam Taj VLSI and Embedded Systems Nitte Meenakshi Institute of Technology Bengaluru India Department of Electronics and Communication Nitte Meenakshi Institute of Technology Bengaluru India Research and Technology Development Group ACE Designers Ltd Bengaluru India

ISBN: (纸本)9789811954818

Inspection of components using machine vision technologies provides solution for quality and process control. This technique is used in various applications such as automotive, pharmaceutical, food and beverage, electronics, packages, process control and special application. The basic idea is to use machine vision technology in the industries to improve the production quality, reduce the scrap product due to non-conformity by controlling the manufacturing process through machine vision and also to prevent the value addition for scrap product in the subsequent stage of manufacturing process. Thus, designing and developing a machine vision system for component identification used the image processing algorithm. Inside the component, small circles which are of 0.5 diameter are detected using Hough circle detection. Graphical user interface was developed for visual representation of the image processing. The training model of the system is developed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graphical user interfaces

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On-Line Monitoring System of Welding Quality Based on machine vision and machine Learning

On-Line Monitoring System of Welding Quality Based on Machin...

引用

2023 IEEE International conference on image processing and Computer applications, ICIPCA 2023

作者： Peng, Genchen Zhang, Liping Lu, Zhijun Shi, Yong Jiangsu Xcmg Construction Machinery Research Institute Co. Ltd Xuzhou China Xcmg Construction Machinery Co. Ltd Road Machinery Branch Xuzhou China

ISBN: (纸本)9798350314670

In this paper, an online monitoring system of welding quality based on machine vision and machine learning was proposed. A high-speed CCD camera was used to monitor the tail end of the molten pool, and the remove small objects algorithm and contour compensation based on convex hull algorithm were utilized to achieve high-precision collection of features such as the width and length of the tail of the molten pool. This effectively solved the technical challenges caused by welding splashes and plasma arc, which could interfere with visual acquisition. Combined with neural network algorithms, a welding quality model was established and validated to accurately identify defects such as welding undercut, welding deviations, and unstable welding processes, with a defect recognition rate of ≥ 94%. © 2023 IEEE.

关键词： machine learning

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Defect detection for CCGA solder column based on machine vision 24

Defect detection for CCGA solder column based on machine vis...

引用

16th International conference on machine Learning and Computing (ICMLC)

作者： Hou, Qili Wang, Yang Chen, Banghua Xu, Song Yang, Lihong 38th Res Inst China Elect Technol Grp Corp Hefei 230088 Peoples R China

ISBN: (纸本)9798400709234

With the characteristics of high I/O packaging density and excellent electrothermal performance, ceramic column grid array (CCGA) packaging has been widely used in highly reliable applications such as aerospace. For CCGA solder column, defect detection needs to be applied before it leaves the factory. The traditional manual detection method has low detection efficiency and the detect accuracy is greatly influenced by human subjective factors. Aiming at this problem, a set of algorithm consists of digital image processing method, Yolov3 network and U-Net network has been combined to realize the surface and inner defect detection for CCGA solder column. The whole algorithm has been embedded into industrial software system based on Qt environment and field experiments have been applied. The experiment results show that the whole algorithm has good real-time performance and the detection accuracy is consistent with manual detection accuracy. The algorithm proposed in this paper can meet the needs of online defect detection for CCGA solder column.

关键词： CCGA solder column defect detection machine vision Yolov3 U-Net

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