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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Khaoula Ben Ali Seifeddine Messaoud Mohamed Ali Hajjaji Mohamed Atri Noureddine Liouane Laboratory of Automatic Signal Image Processing National Engineering School Monastir University Monastir Tunisia Faculty of Sciences of Monastir Monastir University Monastir Tunisia Higher Institute of Applied Sciences and Technology of Sousse Sousse University Sousse Tunisia Computer Engineering Department College of Computer Science King Khalid University Abha Saudi Arabia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Recent years have seen a rapid development in machine Learning, which has profoundly influenced many areas of science and engineering. Among them, computer vision takes the leading place, where important tasks are image classifications powered by CNNs. Despite the great performance of CNNs in complicated scenarios, they remain sensitive to so-called adversarial attacks, and deliberate perturbations leading them to incorrect predictions. Besides more innocuous consequences, this has serious security implications for critical applications, in-cluding medical diagnostics, where misclassifications might result in disastrous outcomes. This research work discusses adversarial attacks on CNNs and other DNNs in computer vision, studying a full range of the generation and detection methods with details while discussing intrinsic vulnerability and robustness. It also proposes a learning framework that will enhance the robustness and security of DNNs and CNNs against such adversarial perils. The ultimate goal is directed to an improvement in the reliability of such models in absolutely critical scenarios for safe deployment into applications where accuracy is crucial.

关键词： Computer vision Accuracy Perturbation methods Computer network reliability machine learning Robustness Security Medical diagnosis Resilience image classification

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Efficient Real-Time Okra Stage Identification using YOLOV8 10

Efficient Real-Time Okra Stage Identification using YOLOV8

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10th IEEE International Conference on Electronics, Computing and Communication Technologies (IEEE CONECCT)

作者： Kumar, Nikhilesh B. Ferbin, F. J. Sivapatham, Shoba Kar, Asutosh Krithiga, R. Vellore Inst Technol Sch Mech Engn Chennai Tamil Nadu India Vellore Inst Technol Ctr Adv Data Sci Chennai Tamil Nadu India Natl Inst Technol Elect & Commun Engn Jalandhar Punjab India Vellore Inst Technol Sch Comp Sci Chennai Tamil Nadu India

ISBN: (纸本)9798350385939;9798350385922

In modern agriculture, crop growth monitoring is a crucial component, as it offers intuitive information about the health and growth of the plant, assisting farmers and other agricultural specialists. This systematic growth monitoring is necessary for crop health and agricultural productivity. We preferred the YOLOv8, which utilizes machine learning and offers efficient plant analysis in agriculture. This preferred method predicts bounding boxes and the probability of each possible class, allowing it to achieve exceptional detection speed without trading off accuracy. Pre-processing was done on the created, "Okra-dataset" to standardize the image to a fixed resolution and enhance our dataset's strength. We tested our work using different models: YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x. Our test revealed that YOLOv8x achieved the highest mean average precision (mAP) of 82.9%. The implementation of research indicates that YOLOv8x is a good tool for agricultural applications, which can be very helpful to farmers.

关键词： Crop growth monitoring computer vision YOLOv8

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Real-time detection of road hazards for autonomous vehicle systems

Real-time detection of road hazards for autonomous vehicle s...

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International image processing, applications and Systems Conference (IPAS)

作者： Máté András Szabó Anita Keszler László Tizedes Machine Perception Research Lab. HUN-REN SZTAKI Budapest Hungary

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Automated detection of road hazards such as speed bumps, has become an important area of research due to its potential to improve road safety in autonomous driving. Various techniques have been introduced to detect these hazards using camera vision and artificial intelligence-based image processing methods. However, estimating their distance is still challenging. To address this problem and to satisfy the requirement for real-time on-board data processing, the proposed system has the following properties: (1) high-accuracy road hazard detection by analyzing mono-images and videos with a re-trained YOLO neural network; (2) precise distance measurement utilizing a LiDAR; and (3) efficient local data processing using ROS, implemented on an NVIDIA Jetson AGX Xavier. An important contribution of this paper is introducing multiple classes of road hazards when training the network, instead of only focusing on speed bumps and potholes. Furthermore we have analyzed different LiDAR technologies (standard rotating and non-repetitive circular scanning) to evaluate and compare their precision and to demonstrate that our method can be successfully applied regardless of the scanning pattern of the LiDAR.

关键词： Laser radar Roads image processing Cameras Data processing Hazards Real-time systems Distance measurement Sensors Autonomous vehicles

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Development and future of information hiding in image transformation domain: A literature review 22

Development and future of information hiding in image transf...

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4th International Conference on image processing and machine vision, IPMV 2022

作者： Yang, Yuer College Of Cyber Security Jinan University China School Of Economics Jinan University China

ISBN: (纸本)9781450395823

Information hiding technology is a technique to hide meaningful information in the public carrier information. When data elements are becoming more and more important, information hiding technology has a better performance than traditional encryption and decryption technologies such as single table substitution and Virginia cipher. Since it contains redundant information, image is a common carrier for information hiding among many carrier types. As one of the primary means of information hiding technology, image transform-domain information hiding technology has been widely studied and used in academic and industrial fields. Information hiding in the image transform domain improves security and robustness effectively. This paper mainly introduces the concepts, principles, processes, mainstream algorithms and applications of image transform-domain information hiding techniques. A possible general future development of it is also depicted. © 2022 ACM.

关键词： image enhancement

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Trends in integration of vision and language research: A survey of tasks, datasets, and methods

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Journal of Artificial Intelligence Research 2021年 71卷 1183-1317页

作者： Mogadala, Aditya Kalimuthu, Marimuthu Klakow, Dietrich Saarland Informatics Campus Saarland University Saarbrücken66123 Germany

Interest in Artificial Intelligence (AI) and its applications has seen unprecedented growth in the last few years. This success can be partly attributed to the advancements made in the sub-fields of AI such as machine learning, computer vision, and natural language processing. Much of the growth in these fields has been made possible with deep learning, a sub-area of machine learning that uses artificial neural networks. This has created significant interest in the integration of vision and language. In this survey, we focus on ten prominent tasks that integrate language and vision by discussing their problem formulation, methods, existing datasets, evaluation measures, and compare the results obtained with corresponding state-of-the-art methods. Our efforts go beyond earlier surveys which are either task-specific or concentrate only on one type of visual content, i.e., image or video. Furthermore, we also provide some potential future directions in this field of research with an anticipation that this survey stimulates innovative thoughts and ideas to address the existing challenges and build new applications. ©2021 AI Access Foundation. All rights reserved.

关键词： Surveys

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An Improved Reversible Data Hiding Scheme Based on Referred Frequencies for a VQ Compressed Index

An Improved Reversible Data Hiding Scheme Based on Referred ...

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machine vision, image processing and Imaging Technology (MVIPIT), International Conference on

作者： Min-Shiang Hwang Kanza Fatima Yu-Lun Wang Chi-Shiang Chan Chia-Chun Wu Department of Computer Science & Information Engineering Fintech and Blockchain Research Center Asia University Taichung Taiwan Department of Computer Science & Information Engineering Asia University Taichung Taiwan Department of Management Information Systems National Chung Hsing University Taichung Taiwan Department of M-Commerce and Multimedia Applications Asia University Taichung Taiwan Department of Computer Science and Information Engineering National Quemoy University Kinmen Taiwan

ISBN: (数字)9798331543037

ISBN: (纸本)9798331543044

Nowadays, we usually compress images before uploading them to social media. However, images on social media can easily be copied, so embedding secret messages in compressed images has become increasingly popular. There are many compression methods, such as Huffman, VQ, ZIP, AMBTC, RAR, JPEG, etc. In this article, we propose an improved data hiding in VQ compression method to achieve better capacity and high quality. Experimental results show that our data-hiding approach is practical.

关键词： Steganography image coding Social networking (online) Vector quantization Bit rate Transform coding Imaging Indexes

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Comparative assessment of common pre-trained CNNs for vision-based surface defect detection of machined components

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EXPERT SYSTEMS WITH applications 2023年第1期218卷

作者： Singh, Swarit Anand Kumar, Aitha Sudheer Desai, K. A. Indian Inst Technol Jodhpur Dept Mech Engn Jodhpur 342030 Rajasthan India

Small and Medium Enterprises (SMEs) and Micro, Small, and Medium Enterprises (MSMEs) contemplate inte-grating machine vision with high throughput manufacturing lines to ensure a consistent quality of standardized components. The inspection productivity can improve considerably by substituting machine vision with manual activities. The pre-trained Convolutional Neural Networks (CNNs) can facilitate enhanced machine vision ca-pabilities compared to the rule-based classical image processing algorithms. However, the non-availability of labeled datasets and lack of expertise in model development restricts their utilities for SMEs and MSMEs. The present work examines the practicality of utilizing publicly available labeled datasets while developing surface defect detection algorithms using pre-trained CNNs considering case studies of typical machined components -flat washers and tapered rollers. It is shown that the publicly available surface defect datasets are ineffective for specific-case such as machined surfaces of flat washers and tapered rollers. The explicitly labeled image datasets can offer better prediction abilities in such cases. A comparative assessment of common pre-trained CNNs is conducted to identify an appropriate network while developing a surface defect detection framework for machined components. The common pre-trained CNNs VGG-19, GoogLeNet, ResNet-50, EfficientNet-b0, and DenseNet-201 showing prediction abilities for similar classification tasks have been examined. The pre-trained CNNs developed using explicit image datasets were implemented to segregate defective flat washers and tapered rollers as sample components manufactured by SMEs and MSMEs. The performance assessment was accomplished using parameters estimated from the confusion matrix. It is observed that EfficientNet-b0 out-performs other networks on most parameters, and it can be preferred while developing a surface defect detection algorithm. The outcomes of the present study form the b

关键词： machine vision Surface defect detection Pre-trained CNNs image classification Labeled datasets

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Research on Two-stage Conveying Bar Counting and Dividing System Based on vision 21

Research on Two-stage Conveying Bar Counting and Dividing Sy...

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21st International Symposium on Distributed Computing and applications for Business Engineering and Science, DCABES 2022

作者： Chen, Guojun Wu, Yang Wuxi Taihu University Jiangsu Key Laboratory of IoT Application Technology Wuxi214064 China College of Computer Internet of Tings Engineering Wuxi Taihu University Wuxi214064 China

ISBN: (纸本)9781665454629

A vision-based automatic bar counting system for two-stage conveying bars is proposed. The system solves the counting problems of sticking and relative sliding of a large number of rebar stacks through image processing algorithms. Through the improvement of the splitting end splitting machine, the design of two-step splitting and visual verification is adopted to achieve accurate splitting of stacked rebars. The system obviously reduces the requirements for installation space, and can make more effective use of the space in the rebar workshop. The system has fast running speed, high accuracy, small changes to the production line, and easy maintenance. It can not only meet the needs of production, but also greatly improve the efficiency of the existing counting system. It is suitable for rebar workshops with limited installation space. The experimental results show that the system can accurately count the bars and can accurately realize the steel separation. For bars with a diameter of 8-12mm, the counting accuracy is over 99.85%, and for bars with a diameter greater than 12mm, the counting accuracy is over 99.96%. © 2022 IEEE.

关键词： image processing

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Expoliting Confidence-Based Model Fusion for Boosting image Classification Accuracy

Expoliting Confidence-Based Model Fusion for Boosting Image ...

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

作者： Xinjian Jiang Department of Computer Science and Technology Nanjing University Nanjing China

In the realm of deep learning, the traditional approach has been to train specialized models for individual tasks, which, although effective, is resource-intensive. The advent of large, universal models has mitigated this issue by offering multitask capabilities, reduced training time, and lower computational costs. However, these generalized models often underperform on specific tasks compared to specialized models. This paper introduces an innovative ensemble approach that integrates specialized and generalized models, specifically focusing on Contrastive Language–image Pretraining (CLIP) and EfficientNet. This work proposes three fusion strategies: Weighted Voting, Confidence Comparison, and Fully Connected Network Fusion, and evaluate them using the CIFAR-100 dataset. The ensemble model significantly outperforms individual models, achieving an adjusted accuracy of up to 0.848. The paper also introduces a novel evaluation metric, Confidence-Accuracy Correlation, to assess the reliability of model confidence. The findings could revolutionize ensemble learning by making it more adaptive and suited for real-world applications, thereby pushing the boundaries of possibility in artificial intelligence.

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Very Efficient Convolutional Neural Network Based on the Discrete Hirschman Transform

Very Efficient Convolutional Neural Network Based on the Dis...

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Asilomar Conference on Signals, Systems & Computers

作者： Weiwei Wang Victor DeBrunner Linda S. DeBrunner Dingli Xue Hanqing Zhao Ranran Tao

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Convolutional Neural Networks (CNNs) play a crucial role in computer vision and machine learning applications, but they are often associated with high computational demands. To tackle this challenge, researchers have turned to the Fast Fourier Transform (FFT) for spectral convolution to help reduce complexity. However, the Discrete Hirschman Transform (DHT) has emerged as a more efficient alternative for performing linear convolutions. In this study, we introduce a novel CNN methodology based on the principles of the DHT. Our experimental results highlight the impressive efficiency of this approach, significantly lowering both computational complexity and processing time. Additionally, we implement the DHT-based method in hardware to validate its performance in real-world applications, demon-strating its effectiveness in practical scenarios.

关键词： Computer vision Fast Fourier transforms Convolution image processing machine learning Hardware Convolutional neural networks Computational complexity Low latency communication Field programmable gate arrays

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