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Enhancement of Safe Machine Cutting Tool Using computer vision

Enhancement of Safe Machine Cutting Tool Using Computer Visi...

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international conference on Futuristic Technologies (INCOFT)

作者： Suryakant I. Atkare Aditya R. Tambe Pratik P. Dhamal Abhijeet M. Malge Amol J. Asalekar Tejas R. Thorat School of Mechanical Engineering MIT Academy of Engineering Alandi Pune Maharashtra India School of Computer Engineering MIT Academy of Engineering Alandi Pune Maharashtra India

Metal cutting operations are essential in various manufacturing processes, but they pose significant safety risks to workers. This research project aims to enhance the safety and efficiency of metal cutting operations through the application of computer vision technology. An intelligent monitoring system will be developed, utilizing cameras and image processing algorithms to detect potential safety hazards in real-time. Through a detailed analysis of current metal-cutting practices, a set of safety measures and best practices will be proposed to reduce accidents and injuries. Personal safety equipment, safe work practices, and secure cutting instruments are some of these precautions. To foster a culture of safety, workers and supervisors will receive training on safe cutting practices and hazard identification. The ultimate goal is to promote safe and efficient metal cutting operations while minimizing the risk of accidents and injuries to workers. By leveraging computer vision technology, the project seeks to significantly enhance worker safety by detecting potential hazards in real-time and enabling immediate intervention and corrective action.

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Research on Korla Fragrant Pear Grading Method Based on Random Forest and MLP

Research on Korla Fragrant Pear Grading Method Based on Rand...

引用

image processing and computer Applications (ICIPCA), IEEE international conference on

作者： Yingchao Wang Jiangyu Zhang Na Li De'an Huang Lihao Qin Shan Sun Xingxing Xie Xinjiang University of Science and Technology Xinjiang Korla China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

In response to the problems of uneven quality and unstable accuracy caused by manual grading in the grading process of Korla pear, a Korla pear grading method based on random forest and MLP is proposed, aiming to further optimize the above problems using machine vision methods. This paper collects left and right views and top views of pear images. Using a combination of K-means clustering and threshold segmentation to achieve background separation; This paper constructs the minimum bounding rectangle for the left and top views of Xiangli to obtain its longitudinal and transverse diameters; Based on the measurement difficulty of pear weight, a pear weight prediction model based on random forest model is constructed using pear diameter parameters; This paper constructs a pear grading model using MLP neural network. The experimental results show that the proposed Korla pear grading method based on random forest and MLP has an accuracy of 99.69% in the training set and 98.75 % in the test set, verifying the feasibility and accuracy of this method.

关键词： Weight measurement Training image segmentation Accuracy Computational modeling Neural networks Object segmentation Manuals Predictive models Random forests

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vision based Interpreter for Sign Languages and Static Gesture Control using Convolutional Neural Network

Vision based Interpreter for Sign Languages and Static Gestu...

引用

international conference on Edge Computing and Applications (ICECAA)

作者： Aayush Rai Agnihotri Deepak Arora Department of Information Technology Amity School of Engineering and Technology Amity University Lucknow Campus Lucknow India

Sign languages are the primary mode of communication for deaf people, and this interpreter aims to provide an effective means of communication between deaf and non-deaf individuals. The proposed system uses computer vision techniques to capture and interpret hand gestures and movements, and OpenCV is used as the primary tool for image processing and analysis. The system's accuracy and efficiency are evaluated using various metrics, and the results demonstrate its effectiveness in real-world scenarios The system aims to assist people with hearing and speech impairments by providing a means of communication through hand gestures and sign languages. The proposed system comprises of a camera module to capture the hand gestures and sign languages, a CNN-based image processing module for gesture recognition, and an OpenCV-based gesture control module for controlling external devices. The CNN-based image processing module is trained on a dataset of hand gestures and sign languages, to recognize and interpret the gestures accurately. The proposed system is evaluated on a set of benchmark datasets and achieved high accuracy in gesture recognition and control. The results of the study suggest that the proposed vision-based interpreter for sign languages and static gesture control can be an effective means of communication for people with hearing and speech impairments. The system recognized static hand postures and hand movements with a 94% accuracy rate. The system achieved an accuracy of 92% in recognizing the sign language gestures. The system's accuracy and robustness make it suitable for real-time applications, and it has the potential to improve the quality of life for people with disabilities.

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Deep Learning Based Hybrid vision Method for Tunnel Liner Falling Block Recognition Research

Deep Learning Based Hybrid Vision Method for Tunnel Liner Fa...

引用

image processing and computer Applications (ICIPCA), IEEE international conference on

作者： Haoxuan Qi School of Civil Engineering Southwest Jiaotong University Chengdu Sichuan China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

This paper presents an innovative approach to enhancing video surveillance and sound source localization within tunnel environments, specifically focusing on the detection of falling blocks. We introduce a robust background subtraction algorithm, the Adaptive Mean and Gaussian Background Subtraction (AMGBS) algorithm, which utilizes grayscale stability intervals and average grayscale values to optimize traditional methods. Additionally, we explore the application of deep neural networks for background modeling, leveraging their ability to learn from training images automatically. A multi-mode background subtraction method is also proposed, which adapts to various lighting conditions through the use of RGB and YCbCr color spaces. To complement visual data, we propose a Time Difference of Arrival (TDOA)-based algorithm for sound source localization, which is particularly effective in tunnel environments where visual detection may be impaired by lighting or obstructions. Through extensive experimentation, including simulated tunnel blockage scenarios, our methods demonstrate significant improvements in accuracy and reliability over existing techniques.

关键词： Location awareness Training Visualization Accuracy Time difference of arrival computer network reliability Lighting Gray-scale Video surveillance Reliability

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Pork Backfat Thickness Detection Based on Complex Background 2

Pork Backfat Thickness Detection Based on Complex Background

引用

2021 2nd international conference on Machine Learning and computer Application, ICMLCA 2021

作者： Chen, YongZe Gao, Cen Wang, JingXiang Li, Xu Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Beijing Zhongke Zhihe Digital Technology Co. Ltd University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783800757398

Intelligence is the inevitable trend and inevitable result of the transformation of various smart factory industries. By designing an intelligent detection algorithm, it is necessary to use computer vision to measure the size of items and discriminate inferior products to reduce accidental errors caused by labor and the cost of machine use in the factory. Based on computer vision and image processing technology, and according to the requirements of online measurement of pig carcass backfat thickness, this paper studies the pig carcass backfat thickness detection algorithm. Tests have proved that this method has a detection accuracy of 94% when the backfat thickness detection error is ±1mm. The detection time of a single sample is 0.36-0.52s, which can meet the needs of online detection. The algorithm in this paper adds image processing methods based on complex backgrounds, including conventional denoising, clustering and segmentation, and finally edge detection algorithms. In horizontal image correction, it uses improved vanishing point correction and vertical contour line correction. The design of the straight line fitting algorithm, the final extraction of the backfat thickness adopts the algorithm design based on straight line pixel detection, which can effectively improve the accuracy of the algorithm. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： computer vision

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Sentiment Analysis of Course Evaluation Comments in Chinese

Sentiment Analysis of Course Evaluation Comments in Chinese

引用

image processing and computer Applications (ICIPCA), IEEE international conference on

作者： Ke Li Fei Hu Teaching Research Institute for Basic Education Chongqing Academy of Education Science Chongqing China School of Intelligent Technology and Engineering Chongqing University of Science and Technology Chongqing China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

A wealth of comments that students provided with online course evaluations can be valuable in helping improve and refine the teaching. However, it is difficult for the computer to utilize these comments and understand student appeal because the comments, expressed in text format, cannot be effectively represented in a way that the computer can recognize and the meaning implied in the comments is always blurred that deepens the difficulty for the computer to understanding them. In this paper, we tackle the representation problem by projecting comments (including words) into a dense vector space, proposing to capture the meaning of the text by exploiting the Gated Recurrent Unit model (GRU) and a real-world dataset of five-year comments (during year 2013–2017) in a university's end-of-course evaluations for the Chinese language. The proposed model is evaluated on the ability to judge emotional tendencies of these comments, discovering useful suggestions and criticisms. Also, we improve the effect of judgement by putting GRU into a highly deep neuron networks and taking into account both the forward and backward association of words in the comment.

关键词： Sentiment analysis Text recognition image processing Computational modeling Neurons Education computer applications Logic gates Vectors

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Research on Artificial Intelligence-Based computer vision and Speech Recognition in Intelligent Automation Systems

Research on Artificial Intelligence-Based Computer Vision an...

引用

image processing and computer Applications (ICIPCA), IEEE international conference on

作者： Jianqin Zhang Nanjing Engineering Branch Jiangsu Union Technical Institute Nanjing China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

This paper focuses on the use of SIFT algorithm for visual feature extraction, and innovatively deeply integrates visual information with voice commands to enable accurate control of intelligent automation systems. Firstly, the robustness and efficiency of SIFT algorithm in complex environment is deeply discussed, and it is applied to target object recognition, scene understanding and other visual tasks to effectively extract key visual features. Further, the audio-visual fusion model is researched and constructed, and the visual features extracted by SIFT algorithm are cleverly integrated with the voice commands processed by advanced speech recognition technology. The synchronous analysis and collaborative decision-making of dual-mode information are realized through artificial intelligence means such as deep learning. The purpose of this model is to improve the comprehensive understanding and response ability of the automated system to multiple and heterogeneous input data, so as to ensure that it can accurately identify user intentions and perform accurate operations in complex environments. Experimental results show that this strategy can significantly improve the environment adaptability, interaction naturalness and task completion accuracy of the intelligent automation system.

关键词： Intelligent automation Deep learning Visualization Accuracy Collaboration Speech recognition Feature extraction Robustness Data models Object recognition

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Review on Evolution of Vehicle Detection Methods in computer vision for Surveillance

Review on Evolution of Vehicle Detection Methods in Computer...

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Emerging Trends in Engineering and Medical Sciences (ICETEMS), international conference on

作者： Ekta Ukey Satishkumar L. Varma Department of Information Technology Pillai College of Engineering Mumbai India

ISBN: (数字)9798331529352

ISBN: (纸本)9798331529390

One major area of computer vision is object detection research that tries to automatically detect and track items of interest in camera video streams. The goal of object detection using video is to provide real-time, accurate, and robust detection and tracking of objects, such as vehicles, people, animals etc., for effective monitoring and security solutions. The ability to detect and track objects in video streams can help to identify potential threats, prevent criminal activity, and improve situational awareness. Appearance-based and motion-based are two categories of object detection. Instead of sitting through endless hours of monitoring the live feed from security cameras, computer vision methods are deployed. There are many challenges associated with object detection including occlusion, cluttered backgrounds, and changes in lighting and weather conditions, avoid traffic on the roads and parking lots and to prevent accidents, a variety of image processing algorithms have been created with the goal of detecting and recognizing moving objects from cameras. A variety of algorithms and techniques are applied to solve these challenges such as neural network based approaches, feature-based approaches, and hybrid approaches that combine multiple techniques have been summarized.

关键词： computer vision Surveillance Roads Neural networks Object detection Streaming media Cameras Real-time systems Security Streams

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Secure Multi-Party Computation with Robust Reversible Watermarking in Encrypted images Using AES Cryptosystem

Secure Multi-Party Computation with Robust Reversible Waterm...

引用

international conference on vision Towards Emerging Trends in Communication and Networking (ViTECoN)

作者： V. Sudharshan Vineeth Surapuneni Sameer Shaik Computer science engineering Khammam institute of technology and sciences Khammam India

Reversible watermarking research has increasingly concentrated on enhancing its robustness to resist attacks during digital media transmission because of the increasing development of network media. In reversible watermarking, the important components are the watermark's durability and the cover image's ability to maintain secrecy. In this paper, a secure multi-party computation (MPC) with robust reversible watermarking in encrypted images is proposed. We can share any text information embedded in an image by encrypting the image with a secret key, and then only the authorized user can access the data. The Pixel Repetition Method is used to perform pre-processing on the image after it has been taken as input. Payload capacity, visual quality, and attack protection are increased by the LSB information hiding algorithm of images using a secret key. It combines information hiding and cryptography to increase security and identity authentication based on digital signature and encryption technology. Its premise is the receiver's extraction of concealed data using the reverse process of System. Finally, through the safety and PSNR (peak signal-to-noise ratio) comparison experiment, the enhanced LSB image steganography algorithm makes use of encryption technology and is better than the standard LSB image steganography method in terms of security and PSNR.

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Research on the Application of Full Convolutional Neural Networks in the Accurate Detection of HD Video Targets in Sports Events

Research on the Application of Full Convolutional Neural Net...

引用

Sensors, Electronics and computer Engineering (ICSECE), IEEE international conference on

作者： Yongping Ma Xinjiang University Urumqi China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

In order to overcome the shortcomings of low accuracy of object detection in existing machine vision, this project intends to use full convolutional neural network to detect the movement of athletes in sports video. The method of sampling, quantization and coding is adopted to realize the digitalization of motion video recording. By smoothing and enhancing sports video and extracting target sports video, the purpose of pre-processing sports video is finally achieved. This project intends to use a full convolutional neural network with jump connectivity and five concave convolutional neural networks with different expansion rates. A variety of jump link methods are used to extract multiple low-level features to achieve the significance edge extraction of multiple objects. Experimental results show that the accuracy and recall rate of moving image and still image are close to 1, indicating that this method has higher detection accuracy and less time.

关键词： Accuracy Smoothing methods Quantization (signal) image edge detection Object detection Feature extraction Sensors Convolutional neural networks Object recognition Sports

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