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Smart Sewer Cleaning machine Design 11

Smart Sewer Cleaning Machine Design

引用

11th IEEE International conference on Consumer Electronics - Taiwan (ICCE-Taiwan) - Empower of Innovative Consumer Technology

作者： Yuan, Shih-Yi Wang, Han-Yu Pai, Hao-Yun Zeng, Hong-Xiang Wang, Wei-Ping Feng Chia Univ Commun Engn Taichung Taiwan

ISBN: (纸本)9798350386851;9798350386844

In Taiwan's modern society, urban infrastructure plays a vital role, and the sewer system is the lifeblood of urban operations. However, due to the complexity and danger of the sewer environment, traditional cleaning methods have great risks and difficulties;manually entering the sewer to clean is not only dangerous, but may also cause labor health problems;the use of chemical cleaners also has environmental and health risks. This new system combines sweeping robots with image analysis and processing to significantly improve sewer cleaning efficiency while reducing risks. The machine has advanced sensing technology and photographic night vision capabilities to safely detect abnormalities and obstacles. Not only does it protect worker safety, it also helps protect the environment and reduces the use of chemical cleaners. This kind of technological innovation will bring about substantial changes in urban management, improve the maintenance efficiency of urban infrastructure, and ensure the stability and sustainability of urban operations.

关键词： Sewer image Recognition Cleaning machine YOLO

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Research on machine vision technology based detection and tracking of objects on video image

Research on machine vision technology based detection and tr...

引用

International conference on image processing, Computer vision and machine Learning (ICICML)

作者： Wang, YiHui Zhang, HuaLiang Shenyang Univ Technol Shenyang Peoples R China Chinese Acad Sci Shenyang Inst Automat Shenyang Peoples R China

ISBN: (纸本)9781665464680

In order to improve the detection and tracking speed of the objects on the video image,shorten the object tracking time,a method of machine vision technology based detection and tracking of the objects on the video image is proposed. The machine vision technology is analyzed and then introduced into the detection and tracking of the objects on the video image. The pixel difference operation is carried out between the video image and its front one to obtain a binary difference video image. The detection of the objects on the video image is completed by adjustment of threshold value. Finally,according to Camshift tracking algorithm flow, the color probability distribution map of the objects on the video image is obtained to complete the object tracking. Experimental results show that the tracking time of this method is significantly shortened compared with the traditional detection and tracking methods.

关键词： object detection object tracking machine vision technology tracking method simulation verification

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Deep Learning Approaches for Dermoscopic image-Based Skin Cancer Diagnosis 7

Deep Learning Approaches for Dermoscopic Image-Based Skin Ca...

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IEEE 7th International conference on Advanced Technologies, Signal and image processing (ATSIP)

作者： Elbedoui, Khouloud Mzoughi, Hiba Ben Slima, Mohamed Sfax Univ Natl Sch Elect & Telecommun Sfax Sfax Tunisia Gabes Univ Natl Engn Sch Gabes Gabes Tunisia UR ATISP Sfax Tunisia

ISBN: (纸本)9798350351491;9798350351484

In recent years, due to UV human exposure, the number of skin cancers 'subjects' cases have been increased, therefore, the accurate detection of malign skin cancer at early stage is considered as very crucial for patients' therapy and to increase the survival rates. Melanomas is considered as the most frequent and dangerous type of skin cancer. Even a huge number of deep-learning (DL) and machine Learning (ML) based-classification methods have been introduced in the literature, there have been suspected cases during the clinical diagnosis of malignant lesions. This paper investigates and explores various DL-based models for an accurate diagnosis and detection of malign and benign skin lesions. Basically, Transfer learning (TL) techniques are adapted to efficient and accurate pre-trained models, mainly EfficientNet-B0-V2 and vision Transformers ViT-b16, on the image-Net datasets. Furthermore, a modified Convolutional Neural Network (CNN) model have been adopted and trained from scratch. A publicly available benchmark dataset has been used in order to evaluate the proposed models 'performances and to compare their effectiveness with state-of-the-arts exiting methods. The obtained results are respectively 79,70%, 86,52%, and 86.97% respectively for CNN, EfficientNet-B0-V2, and ViT-b16 models. The experiments have revealed the effectiveness of our proposed models compared to exiting DL and ML models for classification into benign and malignant skin lesions.

关键词： Melanoma deep learning vision transformers Skin cancer classification CNN

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Computer vision Technologies for Rapid Quality Analysis of Fermentable Biomaterial 17

Computer Vision Technologies for Rapid Quality Analysis of F...

引用

17th IEEE International conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering, TCSET 2024

作者： Lysenko, Vitaliy Lendiel, Taras Pavlov, Serhii Nakonechna, Kateryna Automation and Robotic Systems named by I. Martynenko National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Economic Cybernetics National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798331520564

A computer vision system model for assessing the quality of fermenting biomaterial in a biogas plant has been developed. This model employs computer vision technologies to provide a quantitative and repeatable method for assessing the quality of the material. image separation and processing enables the differentiation of various plant objects in the images, facilitating the assessment of their quantitative and qualitative characteristics. © 2024 IEEE.

关键词： machine vision

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An intelligent measurement method for air preheater clearance 2

An intelligent measurement method for air preheater clearanc...

引用

2nd International conference on image processing, Computer vision and machine Learning, ICICML 2023

作者： Xia, Xianliang Xia, Tian Shanghai Polytechnic University School of Computer and Information Engineering Shanghai China

ISBN: (纸本)9798350331417

To satisfy the needs of thermal power plants for monitoring the boiler air preheater distance between the bottom edge of the fan plate and the rotor ring shroud in high-temperature environments, an intelligent measurement method for air preheater clearance is proposed. First, a set of hardware-assisted schemes combined with computer vision technology is designed, which enables efficient and accurate identification and localization of the measurement target. Then, the pixel distance is remapped using neural network technology to reduce measurement errors caused by image distortion and the placement of camera equipment. The experimental results show that this method is feasible, efficient, and robust, and the measurement accuracy can reach ±1.25mm with a maximum relative error of 1.14 percent. © 2023 IEEE.

关键词： air preheater computer vision monocular ranging neural network

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Synergistic image Encryption Using a Hybrid of Logistic, Tent, and Intermittent Chaotic Mappings 2

Synergistic Image Encryption Using a Hybrid of Logistic, Ten...

引用

2nd International conference on image processing, Computer vision and machine Learning, ICICML 2023

作者： Qu, Yicheng Qin, Zhibao Tsinghua International School Daoxiang Lake Beijing100194 China Communication University of China Beijing100024 China

ISBN: (纸本)9798350331417

The encryption of images is an essential component of ensuring data security in the digital age. Delving into chaotic mappings, our study unveils their robust potential for image encryption. In this paper, we propose a novel scheme for encryption-decryption by merging three chaotic mappings - Logistic, Tent, and Intermittent - into a single scheme. Our rigorous empirical evaluations, spanning histogram uniformity, entropy metrics, and key sensitivity, establish the unparalleled efficacy of our amalgamated approach. © 2023 IEEE.

关键词： chaotic mappings image encryptions Intermittent mapping Logistic mapping tent mapping

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The fusion strategy of multimodal learning in image and text recognition

The fusion strategy of multimodal learning in image and text...

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2024 International conference on Physics, Photonics, and Optical Engineering, ICPPOE 2024

作者： Cheng, Sihao Hou, Ruibo Li, Mingxi University of Illinois Urbana-Champaign ChampaignIL61820 United States

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

Multimodal data such as text and image play an important role in various fields. Traditional machine learning methods often only deal with the data of a single modality, while ignoring the relevance between different modalities, which limits the ability to fully understand and analyze the data. Therefore, multimodal learning emerges as the times require, which aims to improve the effectiveness of data analysis and decision-making by fusing the information of different modal data. In this paper, the basic idea, fusion strategy and specific steps of multimodal learning are described in detail, including data fusion, feature extraction, model training and evaluation. Through a case study, it shows how to combine natural language processing and computer vision technology to solve the joint recognition task of image and text. The importance of selecting a suitable data fusion method is emphasized, and a method for evaluating the effect of multimodal data processing is proposed. © 2025 SPIE.

关键词： Adversarial machine learning

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image classification of electronic components based on vision Transformer 2

Image classification of electronic components based on Visio...

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2nd International conference on image processing, Computer vision and machine Learning, ICICML 2023

作者： Chen, Yuhui Chen, Xiahui Hunan Chemical Vocational Technology College College of Modern Information Engineering Zhuzhou China Shenzhen Kemin Sensor Co. LTD Shenzhen China

ISBN: (纸本)9798350331417

image recognition of electronic components is an important application field, which can be applied to the detection, identification and sequencing of electronic components. In order to solve the problem of electronic component recognition, an image recognition algorithm based on vision Transformer (ViT) is proposed. Traditional image recognition algorithms of electronic components usually use convolutional neural networks, but there are some problems such as long training time and the need to master the global information. During the experiment, transfer learning method and data-enhanced image preprocessing method are used to improve the performance and robustness of the classification network, and the classification accuracy and recognition speed of vision Transformer and traditional convolutional neural network in the classification task of electronic components are tested and compared. The test results show that compared with traditional convolutional neural networks, vision Transformer's self-attention mechanism is more efficient for feature extraction, and the highest recognition accuracy in electronic component image classification tasks can reach 98.4%. In the classification task of small and medium-sized data sets, the vision Transformer network self-attention structure can achieve the same recognition accuracy as the lightweight convolutional neural network ResNet-50, while significantly improving the training speed and classification speed. © 2023 IEEE.

关键词： component the image classification of electronic components transfer learning vision Transformer

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SatSynth: Augmenting image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation

SatSynth: Augmenting Image-Mask Pairs through Diffusion Mode...

引用

IEEE/CVF conference on Computer vision and Pattern Recognition (CVPR)

作者： Toker, Aysim Eisenberger, Marvin Cremers, Daniel Leal-Taixe, Laura Tech Univ Munich Munich Germany NVIDIA Santa Clara CA USA

ISBN: (纸本)9798350353006

In recent years, semantic segmentation has become a pivotal tool in processing and interpreting satellite imagery. Yet, a prevalent limitation of supervised learning techniques remains the need for extensive manual annotations by experts. In this work, we explore the potential of generative image diffusion to address the scarcity of annotated data in earth observation tasks. The main idea is to learn the joint data manifold of images and labels, leveraging recent advancements in denoising diffusion probabilistic models. To the best of our knowledge, we are the first to generate both images and corresponding masks for satellite segmentation. We find that the obtained pairs not only display high quality in fine-scale features but also ensure a wide sampling diversity. Both aspects are crucial for earth observation data, where semantic classes can vary severely in scale and occurrence frequency. We employ the novel data instances for downstream segmentation, as a form of data augmentation. In our experiments, we provide comparisons to prior works based on discriminative diffusion models or GANs. We demonstrate that integrating generated samples yields significant quantitative improvements for satellite semantic segmentation - both compared to baselines and when training only on the original data.

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Airlight Estimation in Underwater image Restoration

Airlight Estimation in Underwater Image Restoration

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2023 International conference on Algorithm, Imaging processing, and machine vision, AIPMV 2023

作者： Chu, Jinlei Zhang, Zhanying Yu, Dongsheng Fang, Weikai Cai, Yi Xu, Chidong Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China

ISBN: (纸本)9781510672444

Underwater imaging is plagued by light absorption and scattering, resulting in distorted, blurry, and low-contrast. This paper introduces an innovative underwater image restoration algorithm that combines natural lighting-based airlight estimation with the refined dark channel prior. The algorithm directly estimates airlight, considering various underwater conditions such as depth, water quality, and camera-object distance, using the Jaffe-McGlamery underwater image formation model tailored for real-world underwater scenarios. A transmission map formula rooted in the refined dark channel prior is then derived. Finally, the algorithm employs the estimated airlight and transmission map to restore the image. Experimental results validate the algorithm's effectiveness in removing airlight artifacts, enhancing image contrast, and providing a clearer and more natural visual output. This approach promises to advance the quality of underwater imaging and its applicability across various domains. © 2024 SPIE.

关键词： image reconstruction

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