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

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

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Networking Systems of AI (INSAI), international conference on

作者： Yuning Wang Beijing Huijia Private School Beijing China

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.

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Comparative Analysis of YOLO, Faster-RCNN and RetinaNet Object Detection Models for Satellite imagery Analysis

Comparative Analysis of YOLO, Faster-RCNN and RetinaNet Obje...

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Cyber Physical Systems, Power Electronics and Electric Vehicles (ICPEEV), international conference on

作者： Amit Kumar Gupta Priya Mathur Shreshtha Mishra Mudita Malav Computer Science and Engineering Manipal University Jaipur Jaipur Rajasthan India Department of Mathematics Poornima Institute of Engineering and Technology Jaipur Rajasthan India

ISBN: (数字)9798350388664

ISBN: (纸本)9798350388671

This study delves into the comparative analysis of two leading object detection models, YOLO, Faster-RCNN and RetinaNet, within the realm of satellite imagery analysis. Utilizing the “Ships/Vessels in Aerial images” dataset from Kaggle, featuring satellite imagery of marine vessels, the research investigates model performance and trade-offs. The dataset comprises images with a characteristic for resolution of 768 × 768 pixels and 3 color channels, later augmented to 416 × 416 pixels. The evaluation includes metrics such as mean Average Precision (mAP), Intersection Over Union (IoU), and processing times. YOLO achieved an IoU of 0.47 and mAP of 0.399, with preprocessing, inference, and post-processing times recorded at 0.4ms, 33.4ms, and 1.6ms, respectively. The Faster-RCNN model attained an IoU of 0.679, F1-Score of 0.543, and mAP50-95 scores of 0.335, 0.560, and 0.680 for small, medium, and large objects, respectively. Conversely, RetinaNet model attained an IoU of 0.971, F1-Score of 0.889, and mAP50-95 scores of 0.044, 0.399, and 0.647 for small, medium, and large objects, respectively. To summarize, this research offers significant perspectives on the characteristics of performance and limitations of YOLO, Faster-RCNN and RetinaNet models in analyzing satellite imagery, laying the groundwork for future developments in computer vision research.

关键词： YOLO Measurement Analytical models Accuracy image resolution Computational modeling Real-time systems Power electronics Satellite images Context modeling

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Generative Adversarial Networks for 3D Scene Reconstruction

Generative Adversarial Networks for 3D Scene Reconstruction

引用

international conference on Computing and Networking Technology (ICCNT)

作者： K. Senthamil Selvan Samaksh Goyal Aravindan M K Omkaresh S. Kulkarni K. Yuvaraj Nitish Vashisht Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai india Quantum University Research Center Quantum University School of Engineering and Technology Mechaical Engineering JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science and Engineering (AI & ML) Vishwakarma Institute of Technology Pune INDIA Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Generative opposed Networks (GANs) are a generative model broadly utilized in device mastering, PC vision, and herbal language processing (NLP). GANs hire neural networks, a generator, and a discriminator that are trained collectively to be able to generate realistic-looking statistics, along with photographs, audio clips, or 3-D scenes. In this painting, we are aware of $3-\mathrm{D}$ scene reconstruction using GAN-based total methods. We aim to generate 3-D scenes from given 2nd pics, allowing us to benefit from insight into the structure and layout of complicated 3-D environments. To this end, we endorse leveraging a method known as inverse rendering to enhance the accuracy of the reconstructed 3-D scenes. We compare our method to the usage of artificial and real-international images, and the effects display the efficacy of our approach. Finally, we talk about capacity destiny guidelines for three-D scene reconstruction and the usage of GAN-based total strategies.

关键词： Three-dimensional displays Shape Neural networks Layout Generative adversarial networks Rendering (computer graphics) Generators image reconstruction Painting Guidelines

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Unsupervised image Generation and Manipulation Using Deep Convolutional Adversarial Networks 1

引用

2nd international conference on Machine Learning and Information processing, ICMLIP 2020

作者： Ghadekar, Premanand Joshi, Shaunak Kokate, Yogini Kude, Harshada Department of Information Technology Vishwakarma Institute of Technology Pune India

ISBN: (数字)9789813348592

ISBN: (纸本)9789813348585

In recent years, there has been an outburst in the field of computer vision due to the introduction of Convolutional Neural Networks. However, Convolutional Neural Networks have been sparsely used for unsupervised learning. The advancement of computational power and large datasets provide large opportunities to apply deep learning for image processing. This paper proposes a new architecture based on Deep Convolutional Generative Adversarial Network (DCGAN) for unsupervised image generation, its usage for image manipulation tasks such as denoising, super-resolution, and deconvolution. This proposed model demonstrates that the learned features can be used for image processing tasks—demonstrating their applications for general use as DCGAN learns from large datasets and adds high-level image details and features where traditional methods cannot be used. While the image results from the proposed network architecture and the original DCGAN architecture are similar in terms of performance, they are visually better when viewed by humans. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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Detection of Corn Leaf Infection Using CNN with Various Optimizers

Detection of Corn Leaf Infection Using CNN with Various Opti...

引用

Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), international conference on

作者： G. R. Sreekanth R. S. Latha R. Roja S. Sankarnanth A. Gayathri Department of Computer Science and Engineering Kongu Engineering College Erode India Department of Computer Technology Kongu Engineering College Erode India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

Corn, a grain categorized within the grass family, stands as a fundamental staple crop globally. It plays a crucial role in supplying sustenance for both humans and livestock, in addition to serving as a raw material for various industries. In 2017, Africa produced 7.4% of the 1135 million tons produced worldwide in 40 million hectares. Throughout its growing process, corn plant is impacted by numerous illnesses. Lack of early detection of its diseases may result in a loss in productivity and even crop failure. It is proposed to analyze the leaves of the corn plant for identifying the diseases by exploiting deep learning methods. A wide range of deep learning algorithms are being utilized to detect the corn leaf with infection with good range of accuracy. To categorize the leaf, image processing models are mostly used for these kind of disease detections. Since it has in-built convolutional layer, CNN can reduce the image with high dimensionality without losing any kind of information. In this proposed work CNN with Adam optimizer has been implemented for better results and achieved 91.68 % of accuracy.

关键词： Deep learning Productivity Crops Medical services Raw materials Convolutional neural networks Internet of Things

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Research on positioning algorithm of binocular camera based on multi-media 2021

Research on positioning algorithm of binocular camera based ...

引用

2nd international conference on Computing and Data Science, CONF-CDS 2021

作者： Xiong, Qianghua Yang, Lei Chen, Qingqiang Dongguan University of Technology School of Computer Science and Technology Dongguan China Dongguan University of Technology School of Electronic Engineering and Intelligence Dongguan China School of Mechatronics and Control Engineering Shenzhen University Dongguan University of Technology Dongguan China

ISBN: (纸本)9781450389570

With the development of computer technology, image processing technology has become an increasing number of mature. However, the accuracy of target positioning has not reached the expectation in the case of multi-medium. Based on the binocular positioning algorithm, Aiming at the binocular positioning algorithm, this paper proposes a multi-medium binocular positioning algorithm. First, from the calibration of the camera, the internal parameters of the camera is obtained and the distortion caused by the camera is removed, and the picture is corrected. Secondly, the position of the target is detected by the YOLOv4 algorithm and the pixels of the target in the picture is obtained. Finally, through the binocular vision algorithm, camera imaging principle and the propagation characteristics of light on the interface of different medium, the spatial position of the target in the world coordinate system is obtained. © 2021 ACM.

关键词： Binoculars

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Multi-Objective Evolutionary for Object Detection Mobile Architectures Search

Multi-Objective Evolutionary for Object Detection Mobile Arc...

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Machine vision, image processing and Imaging Technology (MVIPIT), international conference on

作者： Haichao Zhang Kuangrong Hao Jiashi Li Xin Xia Xuefeng Xiao Shanghai Aerospace Electronic Technology Institute Shanghai P. R. China College of Information Science and Technology Donghua University Shanghai P. R. China ByteDance Intelligent Creation Beijing P. R. China

ISBN: (数字)9798331543037

ISBN: (纸本)9798331543044

Recently, neural architecture search has achieved great success on classification tasks for mobile devices. The backbone network for object detection is usually obtained on image classification tasks. However, the architecture searched through the classification task is sub-optimal because of the gap between the task of classification and object detection. While work focuses on backbone network architecture search for mobile device object detection is limited, mainly because the backbone always requires expensive imageNet pre-training. Accordingly, it is necessary to study the approach of network architecture search for mobile device object detection without expensive pre-training. In this work, we propose a backbone network architecture search algorithm which is a kind of evolutionary optimized method based on non-dominated sorting for NAS scenarios. It can quickly search the backbone network architecture within certain computational complexity constraints. It better solves the problem of suboptimal linear combination accuracy and computational cost. The proposed approach can search the backbone networks with different depths, widths, or expansion sizes via a technique of weight mapping, making it possible to apply the architecture search method for mobile device detection tasks more efficiently. In our experiments, we verify the effectiveness of the proposed approach on YoloX-Lite, a lightweight version of the object detection framework. Under similar computational complexity, the accuracy of the backbone network architecture is 2.0% mAP higher than MobileDet. Our improved backbone network can reduce the computational effort while improving the accuracy of the object detection network. To prove its effectiveness, a series of ablation studies have been carried out and the working mechanism has been analyzed in detail.

关键词： Accuracy Imaging Object detection computer architecture Network architecture Search problems Mobile handsets Neural architecture search Computational complexity Sorting

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image segmentation application combined with DRLSE and moving mesh method 2

Image segmentation application combined with DRLSE and movin...

引用

2nd international conference on computer vision and Data Mining, ICVDM 2021

作者： Lee, Wan-Lung Shi, Hongjian Division of Science and Technology BNU-HKBU United International College Guangdong Zhuhai519087 China

Aiming at the time-consuming problem of level set method in processing image segmentation, this paper presents an image segmentation algorithm combining DRLSE and moving mesh method. This method is applied to segment images into multiple blocks in different initial regions. When a large image needs to be segmented into multiple blocks, the level set method has to calculate various data at each pixel. So, there are problems such as low-efficiency and time-consuming processing. In order to solve this problem, we propose a moving mesh method to process image segmentation based on the level set method. By calculating the monitor function about the image gradient, the grid encryption is automatically realized with the change of image gradient. This makes the calculation of the image segmentation more accurate. In order to improve efficiency, our method will reduce the grid to 1/4. Then the level set method is applied to achieve the result of computational acceleration. In order to verify the performance and accuracy of the new method, our method segmented some images several times. Experiments show that our method can quickly and automatically segment some images into multiple blocks without affecting the results. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： image segmentation

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Ballot character recognition based on image processing 2

Ballot character recognition based on image processing

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2nd international conference on computer vision and Data Mining, ICVDM 2021

作者： Zhao, Yu-Qing Yan, Xiao-Bo Wang, Lin College of Date Science and Information Engineering Guizhou Minzu University Guizhou Guiyang550025 China Key Laboratory of Pattern Recognition and Intelligent Systems of Guizhou Guizhou Guiyang550025 China

According to the relevant provisions of the "Election Law of the People's Republic of China", the engineering software required for the implementation of large-scale conference elections can set voting regulations for "differential elections", "equal elections", and "alternative elections". This paper proposes an automatic ballot recognition system based on image processing, which is suitable for all levels of people’s congresses, party and government meetings or other meetings. It has complete functions and stable recognition. It can automatically count and summarize the voting results and display the voting results on the big screen. Show on the display system. Firstly, the paper ballot is scanned by a scanner to obtain the image of the ballot. Secondly, the system will preprocess the image of the ballot and segment the image into ***, according to the handwritten character recognition algorithm based on image processing and Shen filter proposed in this paper, the handwritten characters "√" and "×" in the subregion are *** final experimental results show that the recognition accuracy of the system is as high as 99.82%, which has an absolute advantage over the traditional manual voting, and can meet the performance requirements of high efficiency, accuracy, objectivity and security of large-scale conference election. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： image segmentation

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computer vision-based demersal fish length measurement technology 2nd

Computer vision-based demersal fish length measurement techn...

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2nd international conference on Sensor Networks and Signal processing, SNSP 2019

作者： Jeng, Sheng-Wen Chiu, Chih-Kai Gan, Kai-Siang Visual Interaction Department Smart Microsystems Technology Center Industrial Technology Research Institute Southern Region Campus Tainan Taiwan

ISBN: (纸本)9789811549168

This study employed underwater cameras and simple spatial mechanism design to capture real-time demersal fish (e.g., Epinephelus lanceolatus or the so-called giant grouper) image stream. computer vision-based algorithm processes and analyzes the captured image to locate a fish inside a bounding box. Sophisticated shape analysis and machine learning technique were then applied to the bounding box region of image to identify and extract the shape contour of the fishes on the image. Next, this contour was used to obtain the location and length of the fishes in image coordinates. Then, by using the spatial information provided to the system earlier (calibration), the scaling information (mm/pixel) can be used to transform the image length to physical scale. Beside, the fish activity also can be calculated based on the difference between current and previous image frame. The application of the research results can provide information for understanding the growing trend of fishes for fish farmers. In addition, the conversion of fish length and weight and detection of fish activity performed during feeding can serve as critical references for precise feeding management to mitigate feed waste and impede water quality deterioration. © Springer Nature Singapore Pte Ltd 2021.

关键词： Fish

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