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A Novel Fire Detection Method Based on Deep Neural Network and image processing 2

A Novel Fire Detection Method Based on Deep Neural Network a...

引用

2nd international conference on Networking Systems of AI, INSAI 2022

作者： Wang, Yuning Beijing Huijia Private School Beijing China

ISBN: (纸本)9781665491785

The development of computer vision and deep neural networks has enabled the accurate classification of objects like fire. Fire can pose a serious threat to humans, so the prevention of fire becomes a major concern for society. In this paper, a trained ResNet-50 model is combined with an RGB-based analysis of fire to achieve higher accuracy. In short, the ResNet-50 model categorizes images quickly, while the RGB model can be adjusted based on the actual environment. They complement each other. In images, the RGB model correctly detects the fire, whereas the ResNet-50 model achieves 96.2% accuracy. © 2022 IEEE.

关键词： Fires

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FFDR: Design and implementation framework for face detection based on raspberry pi 12

FFDR: Design and implementation framework for face detection...

引用

12th Iranian/2nd international conference on Machine vision and image processing, MVIP 2022

作者： Alhajim, Dhafer Akbarizadeh, Gholamreza Ansari-Asl, Karim Islamic University Technical Engineering College Department of Computer Technical Engineering Najaf54001 Iraq Shahid Chamran University of Ahvaz Faculty of Engineering Department of Electrical Engineering Ahvaz Iran Shahid Chamran University of Ahvaz Faculty of Engineering Department of Electrical Engineering Ahvaz Iran

ISBN: (纸本)9781665412162

In today's world, we are surrounded by data of many types, but the abundance of image and video data available offers the data set needed for face recognition technology to function. Face recognition is a critical component of security and surveillance systems that analyze visual data and millions of pictures. In this article, we investigated the possibility of combining standard face detection and identification techniques such as machine learning and deep learning with Raspberry Pi face detection since the Raspberry Pi makes the system cost-effective, easy to use, and improves performance. Furthermore, some images of a selected individual were shot with a camera and a python program in order to do face recognition. This paper proposes a facial recognition system that can detect faces from direct and indirect images. We call this system FFDR, which is characterized by high speed and accuracy in the diagnosis of faces because it uses the Raspberry Pi 4 and the latest libraries and advanced environments in the Python language. © 2022 IEEE.

关键词： Face recognition

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Deep Learning-Based image Style Migration Technique and Art Design

Deep Learning-Based Image Style Migration Technique and Art ...

引用

Algorithm, image processing and Machine vision (AIPMV), international conference on

作者： Kaida Deng Institute of Information Engineering Huizhou Engineering vocational college Huizhou Guangdong China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

In this paper, we adopt image style migration technique based on deep learning, use Vgg19 network for content and style feature extraction, combine an image with art design style, and realise the generation of Van Gogh style Eiffel Tower image. The style conversion effect is evaluated by content loss and style loss calculation, and the values of J_content and J_style show an overall decreasing trend, indicating that the model works well and the output image gradually completes the style migration. This technique is of great significance in the field of computer vision and artificial intelligence, and brings new creative possibilities to the field of art and design, expanding the boundaries of image processing and art and design.

关键词： Deep learning computer vision Art Machine vision Computational modeling Poles and towers Neural networks Market research Feature extraction Artificial intelligence

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Two-Dimensional Improved Otsu Using Histogram Division-Calibration

Two-Dimensional Improved Otsu Using Histogram Division-Calib...

引用

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

作者： Kai Chen Xuan Li Zixu Zhao Soochow University Suzhou China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

Two-dimensional Otsu algorithm is applied widely in image processing and computer vision. However, there are many problems in the original two-dimensional Otsu algorithm. This paper proposes a novel two-dimensional Otsu improved method based on two-dimensional histogram division and calibration method on account of the problem of the improper region division in the traditional algorithm. In this paper, the two dimensional histogram is divided by histogram division and calibration method to demarcate the calculation region. On this basis, the two-dimensional Otsu method is modified to make it more reasonable. The experiments show that this improved two-dimensional Otsu method is more robust and less time-consuming than the algorithms before.

关键词： Resistance Histograms image segmentation computer vision Noise computer applications Calibration

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Stitching Micro images of PCB's using computer vision System 2

Stitching Micro Images of PCB's using Computer Vision System

引用

2nd international conference on Signal and Information processing, IConSIP 2022

作者： Lakhe, Mohini Shinghare, Pratibha College of Engineering Department of Electronics and Telecommunication Pune India

ISBN: (纸本)9781728168852

There is a vast development in manufacturing and packaging technologies, electronic products and equipment now regularly use a variety of precision circuits with large sizes and fine features. At the moment, large-scale circuits have extremely small pitches and line widths that can be several microns. Inspection of printed circuit board faults is thus one of the essential components to offer quality assurance. Assuring PCB quality includes checking for mouse bites, missing holes, spur on routing path, open circuit paths, and short circuit paths. Manual inspection takes a lot of time, and since human beings are limited, manual errors are possible because of the need for manual interventions. We are attempting to automate and construct an intelligent visual inspection system using deep neural networks and computer vision. vision-based analysis of the micro dimensions of a PCB is difficult since a camera can only capture a single view of an image with a limited field of view. To get a wide field of view in this instance, we used picture stitching by registration and fusion. Therefore, this research provides a high-precision micro vision image stitching technique using computer vision and fault detection of PCB using deep learning methodology. © 2022 IEEE.

关键词： computer vision

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An Empirical Study of the Performance of Different Optimizers in the Deep Neural Networks 12

An Empirical Study of the Performance of Different Optimizer...

引用

12th Iranian/2nd international conference on Machine vision and image processing, MVIP 2022

作者： Zohrevand, Abbas Imani, Zahra Kosar University of Bojnord Computer Engineering Department Bojnord Iran

ISBN: (纸本)9781665412162

In recent years, the Stochastic Gradient Descent (SGD) has been commonly used as an optimizer in the Conventional Neural Network (CNN) models. While many researchers have adopted CNN models to classify tasks, to the best of our knowledge, different optimizers developed for CNN have not been thoroughly studied and analyzed in the training CNNs. In this paper, attempts have been made to investigate the effects of the various optimizers on the performance of CNN. Two sets of experiments are conducted. First, for the classification of the records on the CIFAR10, MNIST, and Fashion MNIST datasets, a well-known CNN called VGG11 is trained from scratch by four different kinds of optimizers including SGD, Adam, Adadelta, and AdaGrad. Second, by the same four optimizers, a popular CNN architecture called AlexNet is fine-tuned to classify the Persian handwritten words. In both experiments, the results showed that Adam and AdaGrad have a relatively similar behavior and higher performance in comparison to the other two optimizers in terms of training cost and recognition accuracy. Also, the effect of different values of the initial learning rate on the performance of the Adam optimizer is investigated experimentally. The result revealed that lower values lead to converges more rapidly. © 2022 IEEE.

关键词： Convolutional neural networks

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Light Face: A Light Face Detector for Edge Devices 12

Light Face: A Light Face Detector for Edge Devices

引用

12th Iranian/2nd international conference on Machine vision and image processing, MVIP 2022

作者： Khanehgir, Saeed Mohammad Ghoreyshi, Amir Akbari, Alireza Derakhshan, Reza Sabokrou, Mohammad Part AI Research Center Tehran Iran Sckool of Computer Science of IPM Tehran Iran

ISBN: (纸本)9781665412162

Face detection is one of the most important and basic steps in the recognition and verification of human identity. Using models based on convolutional networks such as face detection models is very difficult and challenging due to a large number of parameters, computational complexity, and high power consumption in environments such as edge devices, mobiles with limited memory storage resources, and low computing power. In this paper, a light and fast face detection model is proposed to predict the face boxes with real-time speed and high accuracy. The proposed model is structured based on the YOLO algorithm and CSPDarknet53 tiny backbone. Some tricks such as calculating custom anchor boxes aimed to solve the detection problem of varying face scales and some optimization techniques such as pruning and quantization have also been used to optimize and reduce the number of parameters and improve the speed to make the final model strong and suitable for use in environments with low computational power. One of our best models with a MAP of 67.52% on the WIDER FACE dataset and a volume of 1.7 Mb and a speed of 1.43 FPS on a mobile phone with ordinary hardware has shown significant performance. © 2022 IEEE.

关键词： Face recognition

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Rapid detection of imperfect wheat grains based on deep learning technique 2

Rapid detection of imperfect wheat grains based on deep lear...

引用

2nd international conference on image processing, computer vision and Machine Learning, ICICML 2023

作者： Hu, Kui Zhang, Hongming Lyu, Bo Shen, Yongcai Fu, Jia Wang, Fudi Lin, Zichao Anhui University Institute of Physical Science and Information Technology Hefei China Chinese Academy of Sciences Institute of Plasma Physics HFIPS Hefei China University of Science and Technology of China Science Island Branch Graduate School Hefei China Hefei Normal University Hefei China

ISBN: (纸本)9798350331417

The detection and identification of imperfect wheat grains are of great significance in evaluating their quality. Manual inspection and separation of imperfect grains in wheat are time-consuming and expensive. Therefore, there is a need for a fast, automated, and accurate method to detect imperfect grains in wheat. In this study, we created an image acquisition platform to capture images of wheat grains. Each image was labeled as either spotted, sprouted, moldy, broken, or perfect grains. To balance calculation efficiency and prediction accuracy, it is necessary to identify a suitable model. Thus, we applied several mainstream deep-learning algorithms, including ResNet50, ResNet50 with an attention module, MobileNetv1, MobileNetv2, MobileNetv3-small, and MobileNetv3-large, to construct a model suitable for practical application. It was found that the MobileNetV3-Small model achieved a good balance between computational efficiency and prediction accuracy. The model based on the MobileNetV3-Small architecture achieved high accuracy, recall rate, and F1-score, all exceeding 96%. Although the MobileNetV3-Small model's accuracy is slightly lower than that of the ResNet50 model with an added attention mechanism, it significantly reduces computational costs, improves detection speed, and cuts prediction time by 50%. Compared with other models, the MobileNetv3-small-based model is more suitable for practical applications due to its advantages of high speed, high precision, and stable prediction performance. This study can provide technical guidance for intelligent recognition and detection of wheat grains in the future. © 2023 IEEE.

关键词： deep learning image pattern recognition MobileNetV3-small Wheat imperfect grains

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Real Time Arabic Sign Language Recognition Using Machine Learning: A vision - Based Approach 2nd

Real Time Arabic Sign Language Recognition Using Machine Lea...

引用

2nd international Libyan conference on Information and Communication Technologies, ILCICT 2023

作者： Elgergeni, Shahd Drawil, Nabil Computer Engineering Department University of Tripoli Tripoli Libya

ISBN: (纸本)9783031626234

Dumb and hearing-impaired people are unable to communicate as well as normal people;thus, they must rely on sign language. Which is a visual or gestural form of communication. Unfortunately, sign language is neither common nor easy to learn. Consequently, deaf-mute people encounter many challenges in their daily communication. Hence, we proposed an intelligent system that employs a vision-based approach dedicated to Arabic sign language recognition (ArSLR). The system is aimed to recognize Arabic words expressed in dynamic sign language expressions and translate them into textural form. While maintaining natural and flexible translation, the system doesn’t impose any hardware requirements, colored gloves, or limitations on the background. A custom dataset is utilized in the development process of the system. A significant amount of experimental work has been conducted to come up with effective and generalized algorithms for image processing, feature extraction, feature selection, and practical classification. Satisfactory results are achieved with the use of linear discriminant analysis (LDA) as a feature selection and dimensionality reduction method;where 100% accuracy is achieved with support vector machine (SVM) and logistic regression classification algorithms. An accuracy of 99.9% is obtained with a multilayer perceptron (MLP) feed-forward artificial neural network and 99.6% with a decision tree classification. The impact of using principal component analysis (PCA) and LDA as a feature selection approach instead of raw data features is also presented. Finally, the system is validated in real-time with unseen data. It is evident from the validation and testing results that word-level ArSLR can be achieved robustly and accurately. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Intelligent systems

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Novel Gaussian Mixture-based Video Coding for Fixed Background Video Streaming 12

Novel Gaussian Mixture-based Video Coding for Fixed Backgrou...

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12th Iranian/2nd international conference on Machine vision and image processing, MVIP 2022

作者： Ghafari, Mohammadreza Amirkhani, Abdollah Rashno, Elyas Ghanbari, Shirin Amirkabir University of Technology Tehran Polytechnic Dept. of Electrical Engineering Tehran Iran Iran University of Science and Technology School of Automotive Engineering Tehran Iran Iran University of Science and Technology Dept. of Computer Engineering Tehran Iran University of Essex Dept. of Computer Science and Electronic Engineering Essex United Kingdom

ISBN: (纸本)9781665412162

In recent years, tremendous advances have been made in Artificial Intelligence (AI) algorithms in the field of image processing. Despite these advances, video compression using AI algorithms has always faced major challenges. These challenges often lie in two areas of higher processing load in comparison with traditional video compression methods, as well as lower visual quality in video content. Careful study and solution of these two challenges is the main motivation of this article that by focusing on them, we have introduced a new video compression based on AI. Since the challenge of processing load is often present in online systems, we have examined our AI video encoder in video streaming applications. One of the most popular applications of video streaming is traffic cameras and video surveillance in road environments which here we called it CCTVs. Our idea in this type of system goes back to fixed background images, where always occupied the bandwidth not efficiently, and the streaming video is related to duplicate background images. Our AI-based video encoder detects fixed background and caches it at the client-side by the background subtraction method. By separating the background image from the moving objects, it is only enough to send the moving objects to the destination, which can save a lot of network bandwidth. Our experimental results show that, in exchange for an acceptable reduction in visual quality assessment, the video compression processing load will be drastically reduced. © 2022 IEEE.

关键词： Video streaming

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