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

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Procedia Computer Science 2024年 251卷 609-614页

作者： Batyrkhan Omarov Bakhytzhan Kulambayev International Information Technology University Almaty 050040 Kazakhstan Turan University Almaty 050040 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.

关键词： CNN Mask R-CNN road damage image processing image analysis"

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A real-time Drone image processing System using deep learning and Big Data Technology

A Real-Time Drone Image Processing System using Deep Learnin...

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IEEE International Conference on Research, Innovation and Vision for the Future

作者： Anh-Tuan Nguyen D. Trong-Hop Do University of Information Technology Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam

ISBN: (数字)9798331505073

ISBN: (纸本)9798331505080

In this research, we delve into advanced image segmentation techniques applied to drone imagery for various environmental and surveillance applications. By leveraging state-of-the-art models such as UNet, deepLabV3, Manet, and Feature Pyramid Network (FPN), our goal is to achieve high precision in segmenting complex aerial scenes. Each of these models possesses unique strengths and weaknesses; hence, we employ an ensemble technique, weighted averaging, to harness their combined capabilities for superior results. Additionally, we incorporate image augmentation techniques to simulate various weather conditions such as haze and raindrops, enhancing the robustness of our models. To manage real-time data efficiently, we implement a streaming pipeline using Apache Kafka and Apache Spark, ensuring scalable and effective processing. Our methods demonstrate significant performance improvements when trained on the original dataset and the combination of original dataset and augmented dataset compared to conventional methods.

关键词： deep learning image segmentation Surveillance Pipelines Big Data Streaming media real-time systems Robustness Drones Meteorology

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Fault diagnosis using signal processing and deep learning-based image pattern recognition

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TM-TECHNISCHES MESSEN 2024年第2期91卷 129-138页

作者： Ren, Zhenxing Guo, Jianfeng Taiyuan Univ Technol Coll Comp Sci & Technol Jinzhong Shanxi Peoples R China Taiyuan Univ Technol Coll Data Sci Jinzhong Shanxi Peoples R China

The vibration signal is a typical non-stationary signal, making it challenging to use traditional time-frequency analysis techniques for fault diagnosis. Therefore, this work investigates the processing of vibration signals and proposes a deep learning method based on processed signals for the fault diagnosis of ball bearings. In this work, the fault diagnosis is formulated as an image classification problem and solved with deep learning networks. The intrinsic mode functions (IMFs), converted from the vibration signals in the time domain, are then transformed into symmetrized dot pattern (SDP) images. In order to increase classification accuracy, the SDP parameters in this study are chosen by optimizing image similarity. The feasibility and accuracy of the proposed approach are examined experimentally.

关键词： fault diagnosis rotating machinery empirical mode decomposition symmetrized dot pattern image similarity pattern recognition

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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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Infield corn kernel detection using image processing, machine learning, and deep learning methodologies under natural lighting

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartE期238卷

作者： Liu, Xiaohang Zhang, Zhao Igathinathane, C. Paulo, Flores Zhang, Man Li, Han Han, Xiongzhe Ha, Tuan Yiannis, Ampatzidis Hak-Jin, Kim Minist Educ Key Lab Smart Agr Syst Integrat Beijing 100083 Peoples R China China Agr Univ Key Lab Agr Informat Acquisit Technol Minist Agr & Rural Affairs Beijing 100083 Peoples R China North Dakota State Univ Dept Agr & Biosyst Engn Fargo ND 58102 USA Kangwon Natl Univ Coll Agr & Life Sci Dept Biosyst Engn Chunchon 24341 South Korea Kangwon Natl Univ Coll Agr & Life Sci Interdisciplinary Program Smart Agr Chunchon South Korea Thai Nguyen Univ Agr & Forestry Tuan M Ha Hitech Agriculture& Forestry R&D Ctr Thai Nguyen City 24119 Vietnam Univ Florida Southwest Florida Res & Educ Ctr Agr & Biol Engn Dept 2685 FL-29 Immokalee FL 34142 USA Seoul Natl Univ Coll Agr & Life Sci Dept Biosyst Engn Seoul South Korea Seoul Natl Univ Coll Agr & Life Sci Convergence Major Global Smart Farm Seoul South Korea

Machine vision has been increasingly used to address agricultural issues. One such case is corn field harvest losses and image-based object detection approaches, namely image processing, machine learning, and deep learning were investigated to detect and count infield corn kernels, immediately after harvest for combine harvester performance evaluation. A hand-held low-cost RGB camera was used to collect images with kernels of different backgrounds, based on which a 420 images dataset (200, 40, and 180 for training, validation, and testing, respectively) was generated. Three different models for kernel detection were constructed based on image processing, machine learning, and deep learning. For the imaging processing method, the images were preprocessed (color thresholding, graying, and erosion), followed by Hough circle detection to identify kernels. For the machine learning (cascade detector) and deep learning (Mask R-CNN, EfficientDet, YOLOv5, and YOLOX), models were trained, validated, and tested. Experimental results showed the overall performance of the deep learning network YOLOv5 was superior to the other approaches, with a small model size (89.3 MB) and a high model average precision (78.3 %) for object detection. The detection accuracy, undetection rate and F1 value were 90.7 %, 9.3 %, and 91.1 %, respectively, and the average detection rate was 55 fps. This study demonstrates that the YOLOv5 model has the potential to be used as a real-time, reliable, and robust method for infield corn kernel detection.

关键词： Infield corn kernel Object detection image processing Machine learning deep learning

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