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Design and Research of Intelligent Jet Fire Extinguishing System Based on STM32 and image recognition Technology 4

Design and Research of Intelligent Jet Fire Extinguishing Sy...

引用

4th IEEE international conference on Data Science and computer Application, ICDSCA 2024

作者： Ma, Xianzhe Hu, Yixin Shao, Muyang Dalian University of Technology Dalian China

ISBN: (纸本)9798350368239

At present, the most widely used large-space automatic jet fire extinguishing system-LRB-automatic fire water cannon) is easily disturbed by sunlight in outdoor environment, resulting in accidental spray or unable to find the fire source. To address these issues, the proposed system integrates ultraviolet (UV) sensors and image recognition technology to accurately detect and extinguish flames, both indoors and outdoors, at a lower cost. the system is divided into multiple modules, including image processing, main control, mobile robot base, and power management. the camera filters visible light interference using a narrow-band filter, while the UV sensor detects open flames, enabling accurate tracking. the water cannon is mounted on a mobile robot, which can autonomously adjust its position to enhance fire-fighting efficiency. the paper highlights the modular design, allowing for easy upgrades without affecting other components. Additionally, the system can be expanded to include automatic patrol capabilities and remote monitoring for broader fire coverage in large spaces. ©2024 IEEE.

关键词： Mobile robots

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Generative Adversarial Networks and their Variants for Precise Fruit recognition 4

Generative Adversarial Networks and Their Variants for Preci...

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4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Desai, Rohini Mano Bhole, Varsha Yogesh Deshpande, Manoj M. A. C. Patil College of Engineering Department of Computer Engineering Navi Mumbai410210 India A. C. Patil College of Engineering Department of Information Technology Navi Mumbai410210 India

ISBN: (纸本)9798331540364

this research study carefully compares various generative adversarial networks (GANs) and their variations in object detection, focusing on fruit recognition in image datasets. the study assesses the efficacy of InfoGAN, CycleGAN, ConditionalGAN, and StyleGAN in enhancing object detection accuracy. To improve object detection models, test each version of GAN by creating fake images. the process is achieved by conducting a lot of experiments with different fruit datasets that include both common and rare varieties. In addition to traditional evaluation metrics like peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), this research introduces a novel approach. the study finds the GAN variant that makes synthetic images work best for finding objects by making a link between PSNR and SSIM values and how well they work for finding objects. the findings offer valuable insights into the relationship between GAN-generated image quality and object detection efficacy, highlighting each GAN variant's strengths and weaknesses in fruit recognition. this comparative analysis serves as a foundation for advancing computer vision systems for object detection tasks and provides direction for future research in this field. © 2024 IEEE.

关键词： cGAN CycleGAN Fruit recognition InfoGAN Object Detection StyleGAN

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A method for estimating the contribution of an object to scene semantics 4

A method for estimating the contribution of an object to sce...

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4th international conference on computer vision and Information Technology, CVIT 2023

作者： Lin, Zhiyuan Zhu, Feng Wang, Jianyu Wang, Qun Zhao, Pengfei Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Faculty of Robot Science and Engineering Northeastern University Shenyang110169 China

ISBN: (纸本)9781510672864

the amount of semantic contribution of each object in an image to understand the scene is different. Understanding the relative importance of objects in a scene for scene semantics is critical for various computer vision applications, such as scene recognition and image captioning. In this paper, we refer to the contribution of an object to scene semantics as the degree of its gist and propose a method for Estimating the degree of the Gist of an Instance (EGoI). In the EGoI method, an object gist degree is estimated by the semantic features comparison strategy and the semantic distance comparison strategy. In the first strategy, the image is represented as a scene graph first, then the aggregation features and the node features of different graph node combinations are calculated to estimate the instance gist that is not in the combination of the nodes. In the second strategy, the captions of the complete and incomplete images are generated, then the semantic distance of these captions is used to estimate the instance gist deleted in the scene. Different strategies for estimating the gist degree of instances are tested in the experiments. the results show that the proposed method can effectively quantify the contribution of an instance to scene semantics. Among these strategies, the method that compares semantic features has better discriminative power for the gist degree of various instances in the scene. © 2024 SPIE.

关键词： Semantics

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A Framework for Video Based Sign Language Interpretation Using Machine Learning and Statistical Methods 4

A Framework for Video Based Sign Language Interpretation Usi...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Swathi, D. Susmetha, K. Sreeji, S. Mangairkarasi, S. B.E Computer Science and Engineering with Data Science Sathyabama Institute of Science and Technology Semmancheri TamilNadu Chennai600119 India Sathyabama Institute of Science and Technology Semmancheri Department of Computer Science and Engineering TamilNadu Chennai6001109 India

ISBN: (纸本)9798331523923

the Sign Language recognition System has been designed to capture video input, process it to detect hand gestures, and translate these gestures into readable text. the project consists of several key components and steps: Video processing: Using OpenCV, the system captures frames from the video input. MediaPipe processes these frames to detect and track hand landmarks in real time. OpenCV capabilities allow for efficient frame extraction and basic image processing tasks such as resizing and normalization. Hand Detection and Tracking: MediaPipe pre-trained models identify and track hand movements within the video frames. the accurate detection and tracking of the hand movements are critical for the subsequent recognition of the sign language gestures. Sign Language recognition: the core system is the deep learning model, trained using the TensorFlow and Keras on a dataset of sign language gestures. the model learns to classify the detected hand movements into corresponding sign language characters or words. Convolutional Neural Networks (CNNs) are typically used for task due to their effectiveness in image recognition tasks. Text Display: Once the system recognizes the signs, it converts them into text and displays the output. this can be done through a console output or a graphical user interface (GUI) built with Tkinter. the GUI provides a user friendly experience, allowing users to see the translated text in real-time. © 2025 IEEE.

关键词： Convolutional neural networks

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Automatic Signboard recognition in Low Quality Night images 8th

Automatic Signboard Recognition in Low Quality Night Images

引用

8th international conference on computer vision and image processing (CVIP)

作者： Kagde, Manas Choudhary, Priyanka Joshi, Rishi Dey, Somnath Indian Inst Technol Indore Indore Madhya Pradesh India Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore Madhya Pradesh India

ISBN: (纸本)9783031581731;9783031581748

An essential requirement for driver assistance systems and autonomous driving technology is implementing a robust system for detecting and recognizing traffic signs. this system enables the vehicle to autonomously analyze the environment and make appropriate decisions regarding its movement, even when operating at higher frame rates. However, traffic sign images captured in inadequate lighting and adverse weather conditions are poorly visible, blurred, faded, and damaged. Consequently, the recognition of traffic signs in such circumstances becomes inherently difficult. this paper addresses the challenges of recognizing traffic signs from images captured in low light, noise, and blurriness. To achieve this goal, a two-step methodology is employed. the first step involves enhancing traffic sign images by applying a modified MIRNet model. In the second step, the YOLOv4 model recognizes traffic signs from enhanced images. the proposed method has achieved a 5.40% increment in mAP@0.5 for low-quality images on YOLOv4. the overall mAP@0.5 of 96.75% has been achieved on the GTSRB dataset. It has also attained mAP@0.5 of 100% on the GTSDB dataset for the broad categories, comparable with the state-of-the-art work.

关键词： Traffic Sign recognition Traffic Sign Detection YOLOv4 Modified MIRNet GTSRB GTSDB

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Research on image Restoration Algorithms Based on Transformer Prediction Networks 4

Research on Image Restoration Algorithms Based on Transforme...

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4th IEEE international conference on pattern recognition and Machine Learning, PRML 2023

作者： Wang, Qingjuan Xiao, Shan Zhou, Yan Wang, Lin Beijing Institute of Technology Zhuhai School of Computing Zhuhai China Macau University of Science and Technology School of Innovative Engineering Macau China

ISBN: (纸本)9798350324303

In recent years, with the rapid development of computer vision, there have been significant advancements in image restoration techniques. However, many current algorithms still struggle with accurately restoring degraded images with lost information. In this paper, we present a new image restoration algorithm called CodeFormerGAN, which is based on the Transformer model. this algorithm consists of three main components: Codebook, CLT network, and CFT. the Codebook module is responsible for learning self-reconstruction and storing high-quality face image parts. the Transformer in the CLT network models the overall composition of the face using low-quality input. the CFT module transforms image features to achieve a flexible balance between restoration quality and fidelity. Additionally, we introduce a GAN-based image super-resolution enhancement algorithm to enhance the sharpness of the restored image. the results of our experiments demonstrate that our proposed method produces higher-quality restoration results and exhibits better robustness when processing images in complex scenes. © 2023 IEEE.

关键词： image reconstruction

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Study of Transformer Model based on ConvNeXt Network for Identification of Potato Leaf Diseases 4

Study of Transformer Model based on ConvNeXt Network for Ide...

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4th international conference on Advances in Electrical Engineering and computer Applications, AEECA 2023

作者： Sun, Jianming Bi, Zhenyu Niu, Lianding Fan, Guoquan Harbin University of Commerce School of Computer and Information Engineering Heilongjiang Harbin150028 China Institute of Cash Crops Heilongjiang Academy of Agricultural Sciences Heilongjiang Harbin150086 China

ISBN: (纸本)9798350308082

this paper proposes a novel hybrid model that combines CNN feature extraction with Transformer to solve the issue of information loss in image processing by vision Transformer and conventional convolutional networks. the study segmented potato leaf images using the GrabCut algorithm, then used the baseline of ConvNeXt network to replace the first layer of 16 × 16 convolution of the original vision Transformer model with a step of 16, and finally used transposed convolution to convert the feature extraction results into the Transformer Encoder expected input size. the Transformer Encoder is then fed the input. the CBAM attention mechanism is then introduced to improve the model's attention to key image regions. the improved model obtains a recognition accuracy of 97.29%, which is 4.22 percentage points higher than before the improvement. Furthermore, the model is robust and adaptable. © 2023 IEEE.

关键词： image recognition

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Research on No-Reference image Aesthetic Quality Assessment Methods 4

Research on No-Reference Image Aesthetic Quality Assessment ...

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4th international conference on Electronic Information Engineering and computer Science, EIECS 2024

作者： Cui, Ying School of Computer Science and Technology Changchun University of Science and Technology Jilin Province Changchun China

ISBN: (纸本)9798331531409

In the digital age, image aesthetic quality assessment is essential in computer vision. We enhance the efficiency of no-reference image aesthetic quality assessment using the No-reference Perceptual Quality Prediction Network (SAP-Net). By incorporating mixed-precision training and a multi-model fusion strategy, we reduce the need for extensive training data and shorten the training cycle. We also introduce the Super-resolution Reconstruction (BSR) architecture in the WBER module to enhance the reconstructed image quality. the loss function structure is optimized, and constraints are added for stability. Spatial attention is improved by increasing the complexity of the residual module and introducing the Multi-scale Spatial Attention mechanism (MSSA) to capture key information at multiple levels. Post-processing of attention maps is added to focus on practical features, reducing redundant information and enhancing evaluation accuracy. the improved SAP- Net algorithm boosts training efficiency by 10% in the IQA-ODI dataset and yields a 166% average improvement in the VAL_PSNR index for reconstructed image performance. Comparative experiments demonstrate that the enhanced SAP-Net algorithm enhances model performance, stability, and prediction accuracy, offering new perspectives and technical support for automated image aesthetic assessment. © 2024 IEEE.

关键词： image quality

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GestureGuard: R-CNN for Sign Langauge 4

GestureGuard: R-CNN for Sign Langauge

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4th international conference on Intelligent Technologies, CONIT 2024

作者： Sethukarasi, T. Sundararajan, Anshika Janavi, S. R.M.K Engineering College Computer Science and Engineering Thiruvallur India

ISBN: (纸本)9798350349900

this paper introduces an innovative system designed to assist individuals with vocal and hearing disabilities by enhancing sign language recognition and converting sign language into text. the project utilizes computer vision techniques and a Region based Convolutional Neural Network (RCNN) model to capture and interpret sign language gestures in real-time. Each frame of the gesture is captured through webcam and the features within the specified region marked by region of interest (ROI) are fed to the two-layer algorithm which comprises of convolution layers and pooling layers. Further to convert from text to sign, Natural Language Toolkit (NLTK) and TextRank algorithm are utilized to condense the sentences which eases the conversion. By tilizing an image-based approach, a user-friendly approach can be deployed easily even on the resource-constrained systems. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep learning-based vehicle tracking and traffic event detection 4

Deep learning-based vehicle tracking and traffic event detec...

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

作者： Sun, Guohao Yan, Kehuan Fan, Chengbin School of Mathematics Tianjin University Tianjing China School of Computer and Data Science Fuzhou University Fujian China School of Electrical Engineering and Automation Xiamen University Tan Kah Kee College Fujian China

ISBN: (纸本)9798350326444

Time-series data mining plays an important role in big data decision making because it can reveal the development pattern of things. Similar concatenation of temporal data is a fundamental prerequisite for data twinning., whose core objective is to find all similar temporal data pairs according to a given similarity metric. Dynamic temporal regularization (DTW) has been widely used in many fields., such as target detection., trend prediction and fault identification., as the best data alignment method on temporal data. We use deep learning to extract vehicle trajectories and perform behavioral pattern learning., and finally use an improved DTW algorithm to pre-process the trajectory data and solve the distance function to achieve matching between the trajectories of the event sequence to be measured and the typical trajectory data patterns. By comparing the indicators with the unimproved DTW algorithm., the research results show that this traffic condition recognition method is stable and reliable., and can maintain high matching accuracy with significantly reduced computation., high success rate and good real-time performance. © 2023 IEEE.

关键词： Data mining

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