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Research on Railway Freight Car Number recognition Method Based on Small Sample 4

Research on Railway Freight Car Number Recognition Method Ba...

引用

4th international conference on Signal processing and computer Science, SPCS 2023

作者： Shao, Yuxiang Feng, Chunsheng Li, Guomin Pu, Sihan School of Computer Science China University of Geosciences Hubei Wuhan China Guangxi Coastal Railway Company Guangxi Nanning China

ISBN: (纸本)9781510672505

the traditional approach of obtaining railway freight car numbers is manually recognizing car numbers, which has the drawbacks of low efficiency and error-prone. In this paper, we propose a small-sample-based method for railway freight car number recognition. the original sample data consists of character samples cropped from the target region of railway freight car numbers, with an average of 15 samples per character. Each type of character sample data is augmented to 500 images and undergoes image processing. Subsequently, an image stitching algorithm is employed to combine the processed character samples into a training set image. Using this training set, a character detection model is trained on the YOLOv5 algorithm. By incorporating spatial positional relationships between car number characters, a car number extraction algorithm is designed to output the final result. We test the proposed method, and our method achieved a car number recognition accuracy of 89.58%, demonstrating credible recognition performance. © 2023 SPIE.

关键词： Object detection

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High Resolution Panoramic Video Synthesis and Objects Detection on Panoramic Video 4

High Resolution Panoramic Video Synthesis and Objects Detect...

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4th international conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2024

作者： Romanova, Karina E. Tsybulko, Evgeniya A. Rudenko, Danila I. Khelvas, Aleksandr V. Gilya-Zetinov, Aleksandr A. Tykhonov, Illya V. Mipt Moscow Russia Intc Intelligent Electronics-Valdai Veliky Novgorod Russia Cos&ht Dolgoprudnii Russia Soccos Moscow Russia Ipmce Moscow Russia

ISBN: (纸本)9798350309508

We propose an approach to the creation of a panorama viewport and objects detection within it in real-time on the base of the set of videos from the assembly of cameras. the task of the panorama viewport generation is significantly different from the panoramic image generation. We perform image stitching only with a part of cameras views (region of interest - ROI) and display the resulting images flow on the user's screen. the complexity of this problem is in processing the images from the set of cameras at the moment, since it is impossible to stitch, process and save the whole panorama in the user device memory. the main practical application of the proposed technology is video surveillance for transportation, industry and public objects (airports, railway stations, sea and river ports, plants, stadiums, etc.). Nowadays, there are many scientific articles exploring panorama stitching methods. However, not many existing algorithms can be used for the panorama stitching in real-time as is. Real-time panorama stitching is the value of the proposed approach. © 2024 IEEE.

关键词： computer vision

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Research on ultrasonic TOFD scanning image weld defect detection method based on PyTorch 4

Research on ultrasonic TOFD scanning image weld defect detec...

引用

4th international conference on Signal processing and computer Science, SPCS 2023

作者： Chen, Yu Chang, Hailin Lai, Jingman Guan, Shenfeng Ju, Jinsan China Agricultural University Dept. of Civil Engineering Beijing100083 China Central Research Institute of Building and Construction Co. Ltd. MCC Beijing100088 China Inspection and Certification Co. Ltd. MCC Beijing100088 China

ISBN: (纸本)9781510672505

In order to overcome the shortcomings of low efficiency and subjective factors in manually analyzing and evaluating weld defect images obtained by the ultrasonic TOFD method. In this study, combined with artificial intelligence image recognition technology, an automatic detection algorithm of ultrasonic TOFD scanning image weld defects based on Yolov6 of PyTorch platform was developed. through continuous optimization of parameter configuration, a recognition model with good recognition effect is obtained. At this time, the recognition and classification of five common defects, namely stoma, slag inclusion, incomplete penetration, lack of fusion, and crackle, are correct on the test set. the correct recognition confidence can reach above 0.9, and the positioning is accurate. © 2023 SPIE.

关键词： Slags

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Comprehensive Weather recognition Using ResNet-50 and a Novel Weather image Dataset 4

Comprehensive Weather Recognition Using ResNet-50 and a Nove...

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4th international Signal processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Wang, Jun Zhou, Yunhan Fu, Min School of Electrical and Electronics Engineering UESTC Chengdu China Cyrinn Science & Technology Ltd. Guangzhou China

ISBN: (纸本)9798331528676

Weather conditions play a crucial role in both daily human activities and industrial operations. For example, recognizing different weather patterns is critical for outdoor automation systems. With the development of AI technology, scientific researchers have trained many models which receive weather images as inputs and output the weather in these images fast and accurately. However, many existing weather recognition models are limited in scope, typically identifying only four types of weather with varying degrees of accuracy. In this study, the two key contributions of this research are: first, the creation of a custom dataset designed specifically for recognizing seven distinct weather conditions, and second, the exploration of multiple machine learning techniques to identify the most effective model for this task. By developing our own dataset, we ensure a broader representation of weather types, allowing the models to train and be tested on more diverse and realistic scenarios. We evaluate five different algorithms: Support Vector Machine (SVM), Random Forest, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and ResNet50. To enhance model generalization and reduce overfitting, we employ data augmentation techniques and add dropout layers in the CNN and ResNet50 architectures. through extensive experimentation, ResNet50 emerges as the best-performing model, achieving an accuracy of approximately 87% on the custom dataset. this work demonstrates the importance of creating domain-specific datasets and using a range of machine learning methods to develop accurate and reliable weather recognition models. Our approach offers a robust solution for outdoor vision systems, with potential applications in autonomous vehicles, surveillance systems, and other environments where accurate weather detection is critical for safety and performance. ©2024 IEEE.

关键词： Convolutional neural networks

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ICPR 2022: Challenge on Harvesting Raw Tables from Infographics (CHART-Infographics) 26

ICPR 2022: Challenge on Harvesting Raw Tables from Infograph...

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26th international conference on pattern recognition / 8th international Workshop on image Mining - theory and Applications (IMTA)

作者： Davila, Kenny Xu, Fei Ahmed, Saleem Mendoza, David A. Setlur, Srirangaraj Govindaraju, Venu Univ Tecnol Centroamer Fac Ingn Tegucigalpa Honduras Univ Buffalo Dept Comp Sci & Engn Buffalo NY 14260 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

the outcomes of the third Challenge on HArvesting Raw Tables from Infographics (ICPR 2022 CHART-Infographics) are presented in this work. Recognizing charts is a difficult process which we divided into the following task: Chart image Classification (Task 1), Text Detection and recognition (Task 2), Text Role Classification (Task 3), Axis Analysis (Task 4), Legend Analysis (Task 5), Plot Element Detection and Classification (Task 6.a), Data Extraction (Task 6.b), and End-to-End Data Extraction (Task 7). We have provided a novel dataset for training reusing all available data from previous challenges, and we also provide a brand new testing dataset for the evaluation of submissions. Both datasets were constructed by manually annotating charts extracted from the Open Access section of the PubMed Central. A total of 9 teams registered out of which 5 submitted results for different tasks of the challenge. Many submissions are based on state-of-the-art methods from computer vision, but the final scores imply that more work will be required to solve the chart recognition problem. the data, annotation tools, and evaluation scripts have been publicly released for academic use.

关键词： Training computer vision image recognition Text recognition Open Access Transformers Data mining

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Workout Mentor: Improving posture in real-time using computer vision 4

Workout Mentor: Improving posture in real-time using compute...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Aiman, Umme Ahmad, Tanvir Jamia Milia Islamia Department of Computer Engineering New Delhi India

ISBN: (纸本)9798350306118

Fitness workouts are tremendously helpful in improving individual health and fitness;yet, if practiced incorrectly by the user, they can be unproductive and potentially harmful. these blunders occur when the person fails to apply the correct posture or position. We propose 'Workout mentor', an application for maintaining proper posture while performing workout routines or yoga. Current work on workout monitoring depends heavily on personal trainers or wearables. Drawing inspiration from medical advancements like robotic surgery, we created an innovative application called 'Workout mentor' that can direct workouts at the ease of your home using a single reference image. Workout mentor employs a machine learning approach to provide a real-time fitness instruction environment. Our research exploits the potential of posture estimation algorithms by providing real-time guidance for workout and yoga enthusiasts. Proposed model achieves 98.33% recognition accuracy. the model utilizes and learns from the crucial components that are necessary for all types of exercise. © 2023 IEEE.

关键词： computer vision

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image processing technology based on computer vision algorithm 4

Image processing technology based on computer vision algorit...

引用

4th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2022

作者： Fan, Mengxia Liu, Yunna Tianjin University of Technology School of Management Tianjin China Tianjin University College of Management and Economics Tianjin China

ISBN: (纸本)9781665463997

Under the background of information age, image processing technology is a commonly used technology in all walks of life. In order to improve the image correction effect, it can be processed based on computer vision algorithm. this paper introduces the image categories and technical characteristics of computer vision algorithm and image processing technology, discusses the image processing technology based on computer vision algorithm, including computer vision display system and image distortion correction two parts, the effect is good, has high application value. In the past, due to the lack of technological development, objects in the traditional two-dimensional environment can only display the side projection, relatively single, in order to adapt to social progress, technology will continue to develop, so three-dimensional three-dimensional picture emerged at the historic moment, a new display technology appeared. In order to obtain the ideal visual effect, technicians need to implement image processing technology on the basis of computer vision algorithm, express the actual coordinates of objects in three-dimensional space with three-dimensional voxels, and correct the distorted image caused by projection. Compared with the traditional BP neural network, the image processing technology based on computer vision algorithm obviously has more advantages and higher accuracy. © 2022 IEEE.

关键词： computer vision

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Classroom head-up rate detection based on RNN-CNN image recognition algorithm 4

Classroom head-up rate detection based on RNN-CNN image reco...

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4th international conference on computer vision, image and Deep Learning, CVIDL 2023

作者： Liang, Qiao Chen, Bingzhi Gao, Zixian Guangdong University of Foreign Studies Guangzhou China South China Normal University Nahai Foshan China

ISBN: (纸本)9798350326444

In recent years, with the popularization of intelligent devices, there have been more and more low-headed individuals in the classroom, seriously affecting the quality of classroom learning. To address this issue, this article first uses high-definition cameras to capture the real-time situation of the classroom, and uses the obtained image information as input samples to input into the neural network algorithm;Secondly, a combination of convolutional neural network (CNN) and recurrent neural network (RNN) was used to improve the accuracy and robustness of head rate detection. Subsequently, this article used a large number of classroom datasets for training and obtained an efficient and reliable head rate detection model through feature extraction, classification, and regression operations on the sample data. Finally, this article provides a detailed analysis and validation of the experimental results. through testing multiple different datasets, we demonstrate the excellent performance of the proposed neural network model in detecting classroom head rates. Compared with traditional methods, this model has higher accuracy and robustness, and can effectively adapt to changes in the head posture of different students and environments. © 2023 IEEE.

关键词： Feature extraction

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Research on Food image recognition of Deep Learning Algorithms 4

Research on Food Image Recognition of Deep Learning Algorith...

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4th international conference on computers, Information processing and Advanced Education, CIPAE 2023

作者： Luo, Lihua Guangdong University of Science and Technology College of Software Engineering Dongguan523000 China

ISBN: (纸本)9798350342710

In recent years, food image recognition has been a research field that has received much attention. Diet is an essential part of people's daily life. through food image recognition and classification, dietary evaluation is conducted to achieve the goal of healthy eating. Manual features and classification methods in traditional target detection algorithms can no longer meet the requirements of detection accuracy and speed, so it is necessary to introduce depth learning-based techniques into food image recognition. Deep learning is a complex machine learning algorithm that allows computers to simulate the human brain's thinking, judge new problems through data sets (human experience), and improve themselves to obtain relatively accurate models, just like humans. Among them, convolutional neural networks have developed most rapidly. they are applied to image recognition with the expectation of being able to automate recognition through machine learning, thereby reducing the workload of image recognition and providing more objective analysis results. Deep learning has achieved great success in intelligent image processing, image classification, and recognition and has excellent development potential and social value. this article implements automatic recognition of food images based on the recognition algorithm of the YOLOv5 convolutional neural network structure, which can detect food images with high detection efficiency. It explores the combination of deep learning to identify food images, providing a reference for people to eat healthily, especially for obese people to control calorie intake, and providing theoretical support for food image recognition. © 2023 IEEE.

关键词： image recognition

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Enhancing Manufacturing Efficiency with computer vision and Doosan Robotics: An Overview of Real-Time Object Detection and Automated Decision-Making 4

Enhancing Manufacturing Efficiency with Computer Vision and ...

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4th international Informatics and Software Engineering conference, IISEC 2023

作者： Chaabani, Ameni Cherif, Raef Yaddaden, Yacine Université du Québec À Rimouski Département de Mathématiques Informatique et Génie Rimouski Canada

ISBN: (纸本)9798350318036

this article presents a solution developed with Doosan Robotics and OnRobot for identifying missing or broken pull tabs in manufacturing processes using computer vision and robotics integration. the solution involves a collaborative robot equipped with a 2.5D camera and an end-effector capable of grasping and manipulating objects. the 2.5D camera captures images of the objects, and computer vision algorithms are applied to detect and identify the presence of pull tabs and assess their condition. the system utilizes advanced image processing techniques to analyze the captured images, identifying any instances of missing or broken pull tabs. Upon detection, the robot employs its end-effector to interact with the identified pull tabs, facilitating their removal or replacement as necessary. the integration also incorporates automated decision-making algorithms to optimize production efficiency, reduce errors, and improve product quality. Integrating these technologies enhances precision, accuracy, and speed in identifying and manipulating objects, leading to increased productivity, cost reduction, and improved product quality. © 2023 IEEE.

关键词： Object detection

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