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CAM: Compound attention module for improved vision models 3

CAM: Compound attention module for improved vision models

引用

2024 3rd International conference on image processing, Object Detection, and Tracking, IPODT 2024

作者： Liu, Houhong Tan, Fei Du, Zhengmiao School of Automation and Information Engineering Sichuan University of Science & Engineering Yibin China Sichuan Provincial Key Laboratory of Artificial Intelligence Yibin China

ISBN: (数字)9781510685529

ISBN: (纸本)9781510685512

In recent years, the attention mechanism has played a significant role in enhancing algorithm performance in deep learning-based visual tasks. Most methods focus on developing more complex attention mechanisms to improve network performance, which inevitably increases the computational complexity of the model. To balance performance and computational complexity, this paper proposes the CAM (Compound Attention Module) attention mechanism, which delivers substantial performance improvements with only a slight increase in parameters. The CAM module operates in two dimensions: space and channel. It is a lightweight, plug-and-play module with minimal computational overhead. We validate our CAM module through extensive experiments on image classification and object detection tasks using the CIFAR-100 and VisDrone2019 datasets. Experimental results demonstrate that the model consistently improves image classification and object detection performance, outperforming similar modules. © 2024 SPIE.

关键词： machine vision

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RECOGNITION OF SURFACE CORROSION MORPHOLOGY ON COASTAL ENGINEERING STRUCTURES USING machine vision TECHNOLOGY 43

RECOGNITION OF SURFACE CORROSION MORPHOLOGY ON COASTAL ENGIN...

引用

ASME 43rd International conference on Ocean, Offshore and Arctic Engineering (OMAE)

作者： Yu, Qifeng Han, Yudong Shanghai Maritime Univ Coll Transport & Commun Shanghai Peoples R China

ISBN: (纸本)9780791887806

The harsh marine atmospheric conditions, including high temperatures, humidity, and salt spray, prevalent in the coastal areas of Hainan, pose a significant challenge to the durability of ground facilities and equipment, often resulting in corrosion and functional degradation. Hence, accurate monitoring of corrosion is paramount for maintaining coastal engineering structures. This study leveraged data from the Chinese National Center for Materials Corrosion and Protection Science to extract corrosion morphology features using image analysis techniques. Subsequently, a corrosion identification model was developed using machine learning algorithms. The model's accuracy was validated through various evaluation metrics. The research outcomes have practical applications in the maintenance and management of coastal engineering structures by providing automatic corrosion morphology recognition. This enables maintenance personnel to promptly undertake repair measures, thereby reducing maintenance costs and enhancing structural sustainability.

关键词： Corrosion morphology machine learning techniques image processing YOLO v5 automatic recognition

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A Graphical Computing-Based image Registration Method for LED Chips 29th

A Graphical Computing-Based Image Registration Method for LE...

引用

29th International conference on Computational and Experimental Engineering and Sciences, ICCES 2023

作者： Zheng, Pengfei Wang, Bo Wan, Xiyuan Lou, Jingjing Lv, Jun Luo, Qingdong Xie, Linsheng Yiwu Industrial and Commercial College Yiwu322000 China East China University of Science and Technology Shanghai200237 China HC Semitek Corporation Yiwu322000 China

ISBN: (纸本)9783031429866

image registration is a key problem and a technical difficulty in the field of machine vision and image processing research. For the problems that the detection accuracy of the machine learning method depends on a large number of training samples, and it is difficult to take samples in the LED chip production process, a graphical computing-based LED chip image registration method is proposed. The geometrical representation of the LED chip image features is used to realize the registration of the chip image in the form of geometric calculation. image morphology processing and Hough transform are used to calculate the registration angle of chip images. With the variational thinking of tessellation grid, a simplified computational model of tessellation grid is proposed to replace the global information with key features of the image for fast registration. The step-by-step generalized image registration algorithm is designed for multiple types of LED chip image characteristics to achieve efficient registration of multiple LED chip images. According to the experimental results of multi-type LED chip image registration, it shows that the proposed graphical computational registration method has a high average registration accuracy and a good registration effect for a variety of LED chip images, which can lay a good foundation for large-scale online detection of LED chip defects. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image registration

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2024 4th International conference on Multimedia processing, Communication and Information Technology, MPCIT 2024 - Proceedings

2024 4th International Conference on Multimedia Processing, ...

引用

4th International conference on Multimedia processing, Communication and Information Technology, MPCIT 2024

ISBN: (纸本)9798350375466

The proceedings contain 60 papers. The topics discussed include: a light weight CNN algorithm for the detection of various tomato plant diseases;a novel convolutional neural network classifier for real-time traffic sign detection;brain controlled robotic car using mind wave;crop sentry: ai-enhanced protection for agriculture field;deep learning-based modular framework for risk mitigation through crowd counting;FPGA implementation of exudates detection in fundus images through machine learning;license plate recognition using federated learning;machine learning driven exploration and identification of steel surfaces: leveraging advanced computer vision techniques;and unified image mastery : an integrated approach to image enhancement, object detection and classification.

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2024 30th International conference on Mechatronics and machine vision in Practice, M2VIP 2024

2024 30th International Conference on Mechatronics and Machi...

引用

30th International conference on Mechatronics and machine vision in Practice, M2VIP 2024

ISBN: (纸本)9798350391916

The proceedings contain 118 papers. The topics discussed include: instantaneous center of rotation trajectory tracking of a novel five-bar robotic exoskeleton for knee rehabilitation;sensitivity-enhanced FBG sensors for human body temperature monitoring;promptable leaf segmentation in plant phenotyping: research perspectives and challenges;transfer learning: an approach on the Riemannian manifold for multiclass motor imagery EEG brain-computer interface;facial instance learning for video-based ASD diagnosis;estimation of the moisture content of in vitro chewed food boluses by image processing;three-dimensional deformation estimation of contacting surface based on a visuotactile sensor;and tendon drawn pronation/supination actuation concept for a rigid assistive forearm exoskeleton.

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Tomato grading based on machine vision D-S multi-feature fusion algorithm 24

Tomato grading based on Machine vision D-S multi-feature fus...

引用

International conference on Algorithms, Software Engineering, and Network Security (ASENS)

作者： Bi, Yanzhi Zhang, Chao Zhong, Peisi Xu, Yang Liu, Mei Zhou, Shufang Shandong Univ Sci & Technol Adv Mfg Technol Res Ctr Qingdao 266590 Shandong Peoples R China Shandong Univ Sci & Technol Coll Mech & Elect Engn Qingdao 266590 Shandong Peoples R China Qingdao Huanghai Univ Coll Intelligent Mfg Qingdao 266555 Shandong Peoples R China

ISBN: (纸本)9798400709784

Aiming at the problem that the detection speed is slow and the classification standard cannot be comprehensively rated when grading tomato quality at present, this paper proposes an improved Sobel edge detection algorithm and multi-feature fusion classification model. Firstly, by combining fast median filtering and morphological operation method, and improving the calculation of the gradient direction of Sobel operator. It can better extract the edge image features of tomato, and provide better image information for subsequent size, maturity and defect detection. Then, a multi-feature fusion algorithm based on D-S evidence theory was proposed. The algorithm process carried out the classification results by D-S evidence theory to calculate a new belief function to assign different weights, and realized a multi-feature fusion classification model. Finally, through the experimental verification, the online detection speed reaches 6 /s, and the average accuracy of tomato quality classification detection is 91%. Compared with the single feature classification, it is found that the multi-feature fusion classification is relatively high in speed and accuracy, which realizes the rapid detection and comprehensive classification of tomato quality classification.

关键词： machine vision image processing Sobel edge detection feature fusion

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FAST CODING MODE PREDICTION FOR INTRA PREDICTION IN VVC SCC 31

FAST CODING MODE PREDICTION FOR INTRA PREDICTION IN VVC SCC

引用

2024 International conference on image processing

作者： Wang, Dayong Yu, Junyi Lu, Xin Dufaux, Frederic Guo, Hongwei Guo, Hui Zhu, Ce Chongqing Univ Posts & Telecommun Chongqing Key Lab Big Data Bio Intelligence Chongqing Peoples R China Wuzhou Univ Guangxi Key Lab Machine Vis & Intelligent Control Wuzhou Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing Peoples R China De Montfort Univ Fac Comp Engn & Media CEM Leicester Leics England Univ Paris Saclay CNRS Cent Supelec Lab Signaux & Syst Gif Sur Yvette France Honghe Univ Sch Engn Mengzi Yunnan Peoples R China Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China

ISBN: (纸本)9798350349405;9798350349399

Currently, screen content video applications are increasingly widespread in our daily lives. The latest Screen Content Coding (SCC) standard, known as Versatile Video Coding (VVC) SCC, employs screen content Coding Modes (CMs) selection. While VVC SCC achieves high coding efficiency, its coding complexity poses a significant obstacle to the further widespread adoption of screen content video. Hence, it is crucial to enhance the coding speed of VVC SCC. In this paper, we propose a fast mode and splitting decision for Intra prediction in VVC SCC. Specifically, we initially exploit deep learning techniques to predict content types for all CUs. Subsequently, we examine CM distributions of different content types to predict candidate CMs for CUs. We then introduce early skip and early terminate CM decisions for different content types of CUs to further eliminate unlikely CMs. Finally, we develop Block-based Differential Pulse-Code Modulation (BDPCM) early termination to improve coding speed. Experimental results demonstrate that the proposed algorithm can improve coding speed by 34.95% on average while maintaining almost the same coding efficiency.

关键词： VVC SCC content type fast coding mode decision BDPCM

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Bridging the machine-Human Gap in Blurred-image Classification via Entropy Maximisation 6

Bridging the Machine-Human Gap in Blurred-Image Classificati...

引用

6th IEEE International conference on image processing, Applications and Systems, IPAS 2025

作者： Sansano-Sansano, Emilio Martinez-Garcia, Marina Portilla, Javier Universitat Jaume I INIT Castellón de la Plana Spain Universitat Jaume I IMAC Castellón de la Plana Spain CSIC Instituto de Óptica Madrid Spain

ISBN: (纸本)9798331506520

Recent studies point to an accuracy gap between humans and Artificial Neural Network (ANN) models when classifying blurred images, with humans outperforming ANNs. To bridge this gap, we introduce a spectral channel-based range-constrained entropy merit function, from which we devise a zero-phase, circular symmetric blind deblurring method. We apply it as a pre-processing step for image classification and test it using pre-trained classification models and images blurred by Gaussian kernels. We compare our method to state-of-the-art restoration methods, showing its superiority, effectively bridging the machine-human gap for most models and blur levels. Our results also rank higher than the competitors in no-reference and full-reference image quality metrics. Notwithstanding the limitation to zero-phase blur, this work shows that, for image pre-processing aimed at visual tasks, it may be advantageous to use merit functions based on vision science and information theory, rather than on the expected error to the latent image. © 2025 IEEE.

关键词： Constrained optimization

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Autonomous Object Detection and Counting using Edge Detection and image processing Algorithms 7

Autonomous Object Detection and Counting using Edge Detectio...

引用

7th International conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Patil, Swati B. Shimpi, Jay Chandrakant Tanawade, Archana Girish Chavan, Pranali Gajanan Tandulkar, Vrushali Shrimant Information Technology Vishawakarma Institute of Information Technology Pune India T and T Infra Ltd Pune India

ISBN: (纸本)9798350397284

machine vision applications are commonly utilised in manufacturing lines as low cost, high precision measuring devices. Output facilities can accomplish high production numbers without mistakes thanks to these solutions that offer contactless control and measurement. A camera may be used to carry out machine vision tasks including product counting., error checking., and dimension measuring. This study makes a recommendation for a vision system application that can do inanimate object item enumeration. The recommended solution uses Otsu thresholding., Hough transformations., edge detection methods., and other image processing algorithms to accomplish automatic counting without taking into account the kind or colour of the product. The system primarily uses one camera. The general idea is to get image with balanced contrast., brightness and appropriate HSV values in it. A picture of the items being captured using camera using android device., and different image processing techniques are then applied to the picture. Further., a real-time machine vision programme was deployed and took photos taken from an actual experimental setup. The practical experiments conducted have shown that the suggested technique yields quick., precise., and trustworthy results based on the comparative study of various detection techniques. © 2023 IEEE.

关键词： Cameras

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Robust Semi-supervised Medical image Classification: Leveraging Reliable Pseudo-labels 1

引用

8th International conference on Computer vision and image processing (CVIP)

作者： Kumar, Devesh Sikka, Geeta Singh, Samayveer Dr BR Ambedkar Natl Inst Technol Dept Comp Sci & Engn Jalandhar Punjab India Natl Inst Technol Dept Comp Sci & Engn Delhi India

ISBN: (数字)9783031585357

ISBN: (纸本)9783031585340;9783031585357

The field of semi-supervised learning (SSL) has fostered new techniques to increase the performance of machine learning models in interpreting medical images. This paper introduces a groundbreaking approach for medical image classification, which combines pseudo-loss approximation and adversarial distortion. Our model improves the learning process by offering a more accurate evaluation of pseudo-labels attached to unlabeled data. It uses pseudo-labeled data that are both trustworthy and meaningful, resulting in higher categorization accuracy. Moreover, adversarial distortion is added to unlabeled data through a cross pseudo-loss approximation strategy. This unique technique allows us to unlock the hidden value in previously ignored data, thereby further boosting our model's performance. We have conducted extensive experiments on two medical datasets, including the NCT-CRC-HE, to illustrate our model's efficacy and adaptability under various test scenarios. Comparative results, showing a consistent performance improvement over other SSL methods, underline the potential of our approach in redefining boundaries in semi-supervised medical image classification tasks, highlighting its promise to significantly contribute to the medical image analysis field.

关键词： pseudo-label approximation medical image analysis Semi-supervised learning adversarial distortion

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