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2023 International Conference on Mechatronics and Intelligent Control, ICMIC 2023

作者： Zhang, Chi Guo, Jiaqi Huanghe S&T University Jiyuan454650 China International College Zhengzhou University Zhengzhou450001 China

ISBN: (纸本)9781510668393

The promotion and utilization of modern technology has made intercultural exchanges more frequent and deep, andsecure image encryption and processing are hot issues for multicultural IOT communication platforms in the context ofglobalization for smart cities. In this paper, the re-al-time image encryption test and resource scheduling optimizationanalysis of multicultural communication platform are carried out based on convolutional neural network. First, weencrypt the image based on the layered image encryption technology of the chaotic system and the Feistel network. Secondly, through different strength encryption algorithms and different convolutional network structures, the encryptedimages are classified and analyzed, and the encryption effect of the images is tested by the classification accuracy. Inaddition, we construct a task-resource scheduling model and design scheduling optimization schemes for the model fromthe aspects of data flow and control flow, so as to realize the mapping of network model to the IOT cloud plat-formandthe reasonable scheduling of task resources on the software and hardware side. © 2023 SPIE.

关键词： deep learning

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real time Quality Detection and Automatic Recognition of Concrete Structures: image processing Algorithm Based on deep learning

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Procedia Computer Science 2024年 243卷 793-800页

作者： Bing Liu College of Intelligent Manufacturing and Automobil Chongqing Vocational College of Transportation Chongqing 402247 China

The safety and durability of concrete structure is an important issue in engineering quality management. In this paper, an image processing algorithm based on deep learning is proposed to realize real-time quality inspection and automatic defect identification of concrete structures. This algorithm uses the Convolutional Neural Network (CNN) to automatically extract the features of concrete surface quality images, and then identify the existence of defects, thus improving the detection efficiency and accuracy. In this paper, for this method, data samples with different specific structures are collected and manually labeled to the data set; then, a multi-layer CNN model with convolution layer, pool layer and full connection layer is designed to train the model, and then image enhancement technology is used to reduce information noise, and data enhancement technology is used to improve the problem-solving ability of the model. In addition, the strategy of Dropout is used to close some nodes to reduce parameters and prevent over-fitting, and the learning rate is adjusted to optimize the classification effect. In addition, this study constructs an all-weather real-time detection framework, including data acquisition, preprocessing, feature extraction, classification and identification and decision-making alarm system, to ensure the rapid positioning of the detection system. To sum up, the results of this study show that the deep learning image processing algorithm has good contrast performance in the field of real-time quality inspection of concrete structures. CNN model has better performance than GAN (Generative Adversarial Network) and LSTM (Long Short-Term Memory) models in detection time, defect identification resolution and detection accuracy. The maximum detection time is 366ms and the shortest is 213 ms. The successful development of this algorithm provides a new method for automatic detection of concrete structure quality, which has important application value

关键词： Concrete Structure real-time Quality Inspection Automatic Recognition deep learning image processing Algorithms

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real-time image processing and deep learning 2022

Real-Time Image Processing and Deep Learning 2022

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real-time image processing and deep learning 2022

ISBN: (纸本)9781510650800

The proceedings contain 20 papers. The topics discussed include: automated detection of common IED components on resource constrained computing devices;closed-loop active object recognition with constrained illumination power;deep learning techniques to identify and classify COVID-19 abnormalities on chest x-ray images;deep learning architecture search for real-time image denoising;self-supervised learning in medical imaging: anomaly detection in MRI using autoencoders;benchmarking the MAX78000 artificial intelligence microcontroller for deep learning applications;high efficiency sensing in real time;a local real-time bar detector based on the multiscale radon transform;object detection on resource-constrained platforms using a configurable ensemble of detectors;comparison of onboard processors for rapid target identification in unmanned aircraft systems;and toward a hardware implementation of lidar-based real-time insect detection.

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deep learning-Based image processing for real-time Detection of Road Surface Damage 15

Deep Learning-Based Image Processing for Real-Time Detection...

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15th International Conference on Emerging Ubiquitous Systems and Pervasive Networks / 14th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, EUSPN/ICTH 2024

作者： Omarov, Batyrkhan Kulambayev, Bakhytzhan International Information Technology University Almaty050040 Kazakhstan Turan University Almaty050040 Kazakhstan

In the rapidly evolving sphere of infrastructure management, early detection of road damage stands paramount for ensuring both safety and longevity. This research introduces an innovative technique for real-time road damage detection by leveraging the Mask R-CNN (Region-based Convolutional Neural Networks) approach. The primary objective was to discern varied forms of damages - from cracks to potholes, ensuring timely interventions and repairs. Utilizing a robust dataset comprising images of multiple road surfaces under different environmental conditions, the Mask R-CNN model was trained exhaustively. Results reveal a commendable accuracy rate, with the model distinguishing between minor aberrations and significant damages adeptly. A distinctive feature was the model's capability to operate in real-time, aiding in instant damage reporting. Furthermore, a comparative analysis with existing methods demonstrated a marked improvement in terms of both detection speed and precision. The findings suggest promising implications for urban planning and road maintenance. The integration of such an approach can revolutionize the manner in which road monitoring is traditionally undertaken, potentially resulting in substantial economic savings and enhanced safety measures. © 2024 The Authors.

关键词： Convolutional neural networks

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Emotion Recognition in Consumers Based on deep learning and image processing: Applications in Advertising

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TRAITEMENT DU SIGNAL 2025年第2期42卷 865-874页

作者： Sun, Liangping Song, Wenting Han, Jie Li, Ayang Qingdao Univ Technol Business Sch Qingdao 266520 Peoples R China

With the continuous advancement of deep learning and image processing technologies, consumer emotion recognition has emerged as a significant area of research in advertising and marketing. Emotional responses from consumers playAa crucial role in optimizing advertising effectiveness and marketing strategies. Among these, micro-expressions- subtle and involuntary facial movements-offer rich emotional cues that can enhance understanding of consumer sentiment. However, existing studies predominantly focus on conventional facial expressions or single-dimensional emotion classification, lacking indepth exploration and accurate detection of micro-expressions. Additionally, current approaches often overlook individual differences and the dynamic nature of emotional changes, resulting in limited accuracy and real-time performance. Effectively leveraging deep learning and image processing for precise emotion recognition thus presents a critical challenge in modern advertising. Traditional methods-based on facial expressions, speech, or physiological signals-face various limitations in practical applications. Facial expression-based models are sensitive to individual variations and rely heavily on the quality of facial feature extraction. Although speech and physiological signal-based techniques can offer valuable emotional insights, constraints in data acquisition and processing hinder their effectiveness in recognizing complex emotional states. This study aims to enhance the precision and real-time capability of consumer emotion recognition by utilizing deep learning and image processing techniques. The key research contributions include: (1) proposing an improved preprocessing method for micro-expression images to enhance emotional feature extraction;(2) designing a deep learning model tailored for micro-expression recognition to optimize emotion classification accuracy;and (3) developing adaptive advertising strategies based on emotion recognition results to maximize adve

关键词： consumer emotion recognition deep micro-expressions advertising emotion recognition model

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Street-Based Parking Lot Detection With image processing And deep learning

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SIGNAL image AND VIDEO processing 2024年第SUPPL 1期18卷 945-952页

作者： Sayar, Ahmet Mustacoglu, Ahmet Fatih Kocaeli Univ Comp Engn Kocaeli Turkiye Istanbul Topkapi Univ Comp Engn Istanbul Turkiye

Due to the rapidly increasing number of vehicles and urbanization, the use of parking spaces on the streets has increased significantly. Many studies have been carried out on the determination of parking spaces by using the lines in the parking areas. However, the usage areas of this method are very limited since these lines are not found in every parking area. In this research, a unique study has been presented to determine the empty and occupied parking spaces in the parking area by processing the images from the cameras located at high points on the streets with depth calculation, perspective transformation and certain image processing techniques within the framework of specific features. Empty and full parking lots were determined by utilizing perspective transformation and depth measurement techniques, and the data obtained were transferred to the real-time Database environment. In addition to determining the parking spaces, the study also aims to inform users through the mobile application and to prevent traffic congestion, extra fuel consumption, waste of time and air pollution caused by fuel consumption.

关键词： image processing deep learning Vehicle detection Smart parking systems Depth analysis

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A deep learning and image processing Pipeline for Object Characterization in Firm Operations

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INFORMS JOURNAL ON COMPUTING 2024年第2期36卷 305-704, C2页

作者： Aghasi, Alireza Rai, Arun Xia, Yusen Oregon State Univ Dept Elect Engn & Comp Sci Corvallis OR 97331 USA Georgia State Univ J Mack Robinson Coll Business Ctr Digital Innovat Atlanta GA 30303 USA Georgia State Univ J Mack Robinson Coll Business Comp Informat Syst Dept Atlanta GA 30303 USA Georgia State Univ Inst Insight J Mack Robinson Coll Business Atlanta GA 30303 USA

Given the abundance of images related to operations that are being captured and stored, it behooves firms to innovate systems using image processing to improve operational performance that refers to any activity that can save labor cost. In this paper, we use deep learning techniques, combined with classic image/signal processing methods, to propose a pipeline to solve certain types of object counting and layer characterization problems in firm operations. Using data obtained by us through a collaborative effort with real manufacturers, we demonstrate that the proposed pipeline method is able to achieve higher than 93% accuracy in layer and log counting. Theoretically, our study conceives, constructs, and evaluates proof of concept of a novel pipeline method in characterizing and quantifying the number of defined items with images, which overcomes the limitations of methods based only on deep learning or signal processing. Practically, our proposed method can help firms significantly reduce labor costs and/or improve quality and inventory control by recording the number of products in real time, more accurately and with minimal up-front technological investment. The codes and data are made publicly available online through the INFORMS Journal on Computing GitHub site.

关键词： image processing layer and object counting machine learning operational efficiency

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An Automated real-time Approach for image processing and Segmentation of Fluoroscopic images and Videos Using a Single deep learning Network

arXiv

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arXiv 2024年

作者： Nguyen, Viet Dung LaCour, Michael T. Komistek, Richard D. University of Tennessee United States

image segmentation in total knee arthroplasty is crucial for precise preoperative planning and accurate implant positioning, leading to improved surgical outcomes and patient satisfaction. The biggest challenges of image segmentation in total knee arthroplasty include accurately delineating complex anatomical structures, dealing with image artifacts and noise, and developing robust algorithms that can handle anatomical variations and pathologies commonly encountered in patients. The potential of using machine learning for image segmentation in total knee arthroplasty lies in its ability to improve segmentation accuracy, automate the process, and provide real-time assistance to surgeons, leading to enhanced surgical planning, implant placement, and patient outcomes. This paper proposes a methodology to use deep learning for a robust and real-time total knee arthroplasty image segmentation. The deep learning model, trained on a large dataset, demonstrates outstanding performance in accurately segmenting both the implanted femur and tibia, achieving an impressive mean Average Precision (mAP) of 88.83 when compared to the ground truth, while also achieving a real-time segmented speed of 20 frames per second (fps). We have introduced a novel methodology for segmenting implanted knee fluoroscopic or x-ray images, which showcases remarkable levels of accuracy and speed, paving the way for various potential extended applications. Copyright © 2024, The Authors. All rights reserved.

关键词： image segmentation

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Performance Evaluation of YOLO-Based deep learning Models for real-time Armour Unit Detection with image Pre-processing Method

Performance Evaluation of YOLO-Based Deep Learning Models fo...

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International Electronics Symposium (IES)

作者： Firmansyah Putra Pratama Alfan Rizaldy Pratama Dewi Mutiara Sari Bayu Sandi Marta R. Haryo Dwito Armono Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Data Science Department Faculty of Computer Science Universitas Pembangunan Nasional Veteran Jawa Timur Surabaya Indonesia Department of Ocean Engineering Faculty of Marine Technology Institut Teknologi Sepuluh Nopember Surabaya Surabaya Indonesia

ISBN: (数字)9798350391992

ISBN: (纸本)9798350392005

Breakwater construction in Indonesia still relies on divers to direct the placement of rock armour units, which is risky and time-constrained. This research aims to replace the diver's task with a deep learning-based vision system using YOLO-based deep learning models. The system utilizes image pre-processing technology by applying histogram equalization (HE) techniques to improve image quality before the detection process. This research evaluates the performance of the YOLO-based deep learning models in detecting armour units in real-time with a focus on various environmental conditions, which are clear and murky water. The analysis reveals clear water consistently supports higher average frame rates (FPS) compared to murky water, maintaining efficient frame processing across all models. In murky water, histogram equalization significantly enhances detection accuracy from 60% to 80% for YOLOv4-tiny and YOLOv7-tiny, demonstrating its effectiveness in challenging conditions. Notably, accuracy remains at 100% for all models in clear water, underscoring their robust performance under optimal visibility conditions.

关键词： deep learning Performance evaluation image quality Histograms Analytical models Accuracy Machine vision

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Special issue on deep learning for emerging embedded real-time image and video processing systems

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JOURNAL OF real-time image processing 2021年第4期18卷 1167-1171页

作者： Jeon, Gwanggil Chehri, Abdellah Incheon Natl Univ Incheon South Korea Univ Quebec Chicoutimi Chicoutimi PQ Canada

Experiments on public datasets suggest that this method certifies its effectiveness, reaches human-level performance, and outperforms current state-of-the-art methods with 92.8% on the extended Cohn-Kanade (CK+) and 87.0% on FERPLUS. “A locally-processed light-weight deep neural network for detecting colorectal polyps in wireless capsule endoscopes” propose a light-weight DNN model that has the potential of running locally in the WCE [2]. [...]only images indicating potential diseases are transmitted, saving energy on data transmission. Background subtraction is a substantially important video processing task that aims at separating the foreground from a video in order to make the post-processing tasks efficient. [...]several different techniques have been proposed for this task but most of them cannot perform well for the videos having variations in both the foreground and the background. “Background subtraction in videos using LRMF and CWM algorithm,” a novel background subtraction technique is proposed that aims at progressively fitting a particular subspace for the background that is obtained from L1-low rank matrix regularization using the cyclic weighted median algorithm and a certain distribution of a mixture of Gaussian noise for the foreground [3].

关键词： image processing and Computer Vision Multimedia Information Systems Computer Graphics Pattern Recognition Signal image and Speech processing

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