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8th International Conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2024

作者： Dong, Xianfa Wang, Xuezhong Beijing Institute of Petrochemical Technology College of New Materials and Chemical Engineering Beijing China

ISBN: (纸本)9798331506230

Froth flotation is an important process in the mineral processing industry for extracting valuable materials. This work investigates online microscopic imaging and machine learning based image analysis methods for real-time monitoring of the process. Previous limited work explored imaging the foam at the top surface layer of the froth flotation process. The new process imaging system in this work uses a corrosion-resistant online real-time imaging probe that can be put into the inside of the slurry and capture real-time images of bubbles. The acquired images are analyzed online using deep learning algorithms to automatically obtain key parameters of the bubbles, providing valuable data for froth flotation research and process control. © 2024 IEEE.

关键词： Froth flotation

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A computational method for real-time roof defect segmentation in robotic inspection

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COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING 2025年

作者： Zhao, Xiayu Jebelli, Houtan Univ Illinois Dept Civil & Environm Engn Champaign IL USA

Roof inspections are crucial but perilous, necessitating safer and more cost-effective solutions. While robots offer promising solutions to reduce fall risks, robotic vision systems face efficiency limitations due to computational constraints and scarce specialized data. This study presents real-time roof defect segmentation network (RRD-SegNet), a deep learning framework optimized for mobile robotic platforms. The architecture features a mobile-efficient backbone network for lightweight processing, a defect-specific feature extraction module for improved accuracy, and a regressive detection and classification head for precise defect localization. Trained on the multi-type roof defect segmentation dataset of 1350 annotated images across six defect categories, RRD-SegNet integrates with a roof damage identification module for real-time tracking. The system surpasses state-of-the-art models with 85.2% precision and 76.8% recall while requiring minimal computational resources. Field testing confirms its effectiveness with F1-scores of 0.720-0.945 across defect types at processing speeds of 1.62 ms/frame. This work advances automated inspection in civil engineering by enabling efficient, safe, and accurate roof assessments via mobile robotic platforms.

关键词： image segmentation

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deep learning-accelerated image reconstruction in MRI of the orbit to shorten acquisition time and enhance image quality

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JOURNAL OF NEUROIMAGING 2024年第2期34卷 232-240页

作者： Estler, Arne Zerweck, Leonie Brunnee, Merle Estler, Bent Hauser, Till-Karsten Richter, Vivien Oergel, Anja Buerkle, Eva Becker, Hannes Hurth, Helene Stahl, Stephane Konrad, Eva-Maria Kelbsch, Carina Ernemann, Ulrike Gohla, Georg Univ Hosp Tuebingen Dept Radiol Diagnost & Intervent Neuroradiol Tubingen Germany Heidelberg Univ Hosp Neurol Univ Clin Dept Neuroradiol Heidelberg Germany Heidelberg Univ Hosp Dept Cardiol Angiol & Pneumol Heidelberg Germany Univ Tubingen Dept Neurosurg Tubingen Germany Centerplast Private Practice Saarbrucken Germany Univ Tubingen Univ Eye Hosp Ctr Ophthalmol Tubingen Germany Hoppe Seyler Str 3 D-72076 Tubingen Baden Wurttembe Germany

Background and Purpose: This study explores the use of deep learning (DL) techniques in MRI of the orbit to enhance imaging. Standard protocols, although detailed, have lengthy acquisition times. We investigate DL-based methods for T2-weighted and T1-weighted, fat-saturated, contrast-enhanced turbo spin echo (TSE) sequences, aiming to improve image quality, reduce acquisition time, minimize artifacts, and enhance diagnostic confidence in orbital ***: In a 3-Tesla MRI study of 50 patients evaluating orbital diseases from March to July 2023, conventional (TSES) and DL TSE sequences (TSEDL) were used. Two neuroradiologists independently assessed the image datasets for image quality, diagnostic confidence, noise levels, artifacts, and image sharpness using a randomized and blinded 4-point Likert ***: TSEDL significantly reduced image noise and artifacts, enhanced image sharpness, and decreased scan time, outperforming TSES (p < .05). TSEDL showed superior overall image quality and diagnostic confidence, with relevant findings effectively detected in both DL-based and conventional images. In 94% of cases, readers preferred accelerated ***: The study proved that using DL for MRI image reconstruction in orbital scans significantly cut acquisition time by 69%. This approach also enhanced image quality, reduced image noise, sharpened images, and boosted diagnostic confidence.

关键词： acquisition time deep learning reconstruction deep resolve boost image processing image quality magnetic resonance imaging orbital imaging

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A Graphical deep learning Approach to RF Fingerprinting in the time-Frequency Domain

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IEEE SENSORS JOURNAL 2024年第10期24卷 16984-16990页

作者： Li, Bo Cetin, Ediz Macquarie Univ Sch Engn Sydney NSW 2109 Australia

The emergence of the Internet of Things (IoT) as a global infrastructure of interconnected network of heterogeneous wireless devices and sensors is opening new opportunities in myriad of applications. This growing pervasiveness of IoT devices, however, is leading to growing concerns regarding security and privacy. Radio Frequency (RF) fingerprinting techniques operating at the physical layer can be used to provide an additional layer of protection to ensure trustworthy communications between devices to address these concerns. We present a graphical deep learning approach in the time-frequency (TF) domain based on short-time Fourier transform (STFT) where the intensity information of the STFT is used to generate 2-D image inputs for training and testing of the deep learning models for RF fingerprinting and identification. The performance of the proposed approach is evaluated and compared with the baseline approach operating in the waveform domain, using the same neural network architecture based on over-the-air captured datasets from 12 Zigbee devices. The experimental results show that the proposed approach outperforms both baseline approach, achieving nearly 100% identification accuracy based on data captured in a makeshift RF chamber, and accuracy of 98% on the dataset which was captured under real-life conditions, demonstrating the robustness of the proposed approach. Furthermore, the impact of the STFT-parameter selection on the identification performance of the proposed approach is also evaluated.

关键词： deep learning Radio frequency Fingerprint recognition time-frequency analysis Object recognition Sensors Internet of Things Internet of Things (IoT) radio frequency (RF) fingerprinting software-defined radio wireless device identification

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A Hybrid deep learning-Based Framework for Chip Packaging Fault Diagnostics in X-Ray images

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第9期20卷 11181-11191页

作者： Wang, Jie Li, Gaomin Bai, Haoyu Yuan, Guixin Li, Xuan Lin, Bin Zhong, Lijun Zhang, Xiaohu Sun Yat Sen Univ Sch Aeronaut & Astronaut Guangzhou 510725 Peoples R China China Aerosp Components Engn Ctr Beijing 100094 Peoples R China Fujian Normal Univ Coll Photon & Elect Engn Fuzhou 350117 Peoples R China

In the testing of chips, defect diagnostics in X-ray images of packaging chips is mainly performed by humans, which is time-consuming and inefficient. To overcome the abovementioned problems, a novel intelligent defect diagnostics system based on hybrid deep learning for chip X-ray images was proposed. The system consists of four successive stages: image segmentation and normalization, image reconstruction and defect detection, contour matching, and qualification diagnosis. The first stage is used to localize the external contours of the target chip and remove extraneous backgrounds through the improved UNet. Then, considering the variety of defects and the complexity of labeling, an unsupervised learning model is designed to reconstruct defect-free images to detect defects, which requires only normal samples for training. Third, the multicomponent template matching based on structural prior is used to localize the internal contours of the chip. In the final stage, the qualification is diagnosed based on the previous results through the Floyd-Warshall algorithm. The effectiveness and robustness of the proposed methods are verified by experiments on real-world inspection lines. The experimental results demonstrate that the developed system can successfully perform fault diagnostics tasks, achieving a judgment accuracy of 92.5%.

关键词： X-ray imaging Feature extraction image segmentation Inspection Defect detection image reconstruction Packaging deep learning defect diagnostics unsupervised learning X-ray images

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Auto-BCS: A Hybrid System for real-time Breast Cancer Screening from Pathological images

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JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024年第4期37卷 1752-1766页

作者： Ekta, Vandana Bhatia, Vandana Netaji Subhas Univ Technol Delhi India

Breast cancer is recognized as a prominent cause of cancer-related mortality among women globally, emphasizing the critical need for early diagnosis resulting improvement in survival rates. Current breast cancer diagnostic procedures depend on manual assessments of pathological images by medical professionals. However, in remote or underserved regions, the scarcity of expert healthcare resources often compromised the diagnostic accuracy. Machine learning holds great promise for early detection, yet existing breast cancer screening algorithms are frequently characterized by significant computational demands, rendering them unsuitable for deployment on low-processing-power mobile devices. In this paper, a real-time automated system "Auto-BCS" is introduced that significantly enhances the efficiency of early breast cancer screening. The system is structured into three distinct phases. In the initial phase, images undergo a pre-processing stage aimed at noise reduction. Subsequently, feature extraction is carried out using a lightweight and optimized deep learning model followed by extreme gradient boosting classifier, strategically employed to optimize the overall performance and prevent overfitting in the deep learning model. The system's performance is gauged through essential metrics, including accuracy, precision, recall, F1 score, and inference time. Comparative evaluations against state-of-the-art algorithms affirm that Auto-BCS outperforms existing models, excelling in both efficiency and processing speed. Computational efficiency is prioritized by Auto-BCS, making it particularly adaptable to low-processing-power mobile devices. Comparative assessments confirm the superior performance of Auto-BCS, signifying its potential to advance breast cancer screening technology.

关键词： Breast cancer screening deep learning Classification models Hybrid system On-device image recognition

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Biomedical image segmentation algorithm based on dense atrous convolution

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2024年第3期21卷 4351-4369页

作者： Li, Hong'an Liu, Man Fan, Jiangwen Liu, Qingfang Xian Univ Sci & Technol Coll Comp Sci & Technol Xian 710054 Peoples R China Shijiazhuang Posts & Telecommun Tech Coll Informat Ctr Ctr Management Shijiazhuang 050021 Peoples R China Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China

Biomedical images have complex tissue structures, and there are great differences between images of the same part of different individuals. Although deep learning methods have made some progress in automatic segmentation of biomedical images, the segmentation accuracy is relatively low for biomedical images with significant changes in segmentation targets, and there are also problems of missegmentation and missed segmentation. To address these challenges, we proposed a biomedical image segmentation method based on dense atrous convolution. First, we added a dense atrous convolution module (DAC) between the encoding and decoding paths of the U -Net network. This module was based on the inception structure and atrous convolution design, which can effectively capture multi -scale features of images. Second, we introduced a dense residual pooling module to detect multi -scale features in images by connecting residual pooling blocks of different sizes. Finally, in the decoding part of the network, we adopted an attention mechanism to suppress background interference by enhancing the weight of the target area. These modules work together to improve the accuracy and robustness of biomedical image segmentation. The experimental results showed that compared to mainstream segmentation networks, our segmentation model exhibited stronger segmentation ability when processing biomedical images with multiple -shaped targets. At the same time, this model can significantly reduce the phenomenon of missed segmentation and missegmentation, improve segmentation accuracy, and make the segmentation results closer to the real situation.

关键词： biomedical image segmentation deep learning dense atrous convolution dense residual pooling multi-scale features attention mechanism

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An efficient deep learning-based framework for image distortion correction

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VISUAL COMPUTER 2024年第10期40卷 6955-6967页

作者： Li, Sicheng Chu, Yuhui Zhao, Yunpeng Zhao, Pengpeng Zhejiang Univ Innovat Ctr Smart Med Technol & Devices Binjiang Inst Hangzhou 310053 Peoples R China Zhejiang Univ Dept Biomed Engn Zhejiang Prov Key Lab Cardio Cerebral Vasc Detect Hangzhou 310027 Peoples R China Endoview Med Technol Co Ltd Suzhou 215300 Peoples R China

Geometric distortions in digital images, caused by factors such as lens defects and changes in camera angles, substantially influence the fidelity of the image by altering pixel positions and shapes. Current geometric distortion correction methods, focusing on specific types of distortions and relying on high computational resources, face limitations in universality and practicality across diverse real-world applications. We propose here a two-stage distortion correction method that integrates deep learning with traditional image registration algorithms for correcting multiple types of geometric distortion. Compared to state-of-the-art correction methods, our proposed method demonstrates flexibility, capable of addressing a wide range of geometric distortions and achieves superior correction results with fewer parameters. In addition, tests performed on synthetic datasets show an improvement of 10.39% for PSNR, 30.42% for SSIM, and 85% for processing speed, compared to the best performing methods to our knowledge. Finally, experiments with handheld medical endoscopic scanners confirm the applicability and robustness of our method in real-world scenarios. Our method offers a versatile and efficient solution for geometric distortion correction, suitable for various applications, including medical imaging and resource-limited embedded systems. Code is available at https://***/MaybeRichard/EffiGeoNet

关键词： Digital image correlation Geometric distortion Distortion correction Optical imaging image registration Lens distortion

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Efficient Remote Health Monitoring Using deep learning and Parallel Systems

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第10期15卷 174-186页

作者： El Khadiri, Zakaria Latif, Rachid Saddik, Amine Ibn Zohr Univ Agadir Natl Sch Appl Sci Lab Syst Engn & Informat Technol LISTI Agadir Morocco Ibn Zohr Univ Fac Appl Sci Ait Melloul Morocco

This study presents a novel approach for non- contact extraction of physiological parameters, such as heart rate and respiratory rate, from facial images captured using RGB cameras, leveraging recent advancements in deep learning and signal processing techniques. The proposed system integrates Artifacts intelligent-driven algorithms for accurately estimating vital signs, addressing key challenges such as variations in lighting conditions, facial orientation, and noise. The system is implemented on both a naive homogeneous architecture and an optimized heterogeneous CPU-GPU system to enhance real-time performance and computational efficiency. A comparative analysis is performed to evaluate processing speed, accuracy, and resource utilization across both architectures. Results demonstrate that the optimized heterogeneous system significantly outperforms the homogeneous counterpart, achieving faster processing times while maintaining high accuracy levels. This performance boost is achieved through parallel computing frameworks such as OpenMP and OpenCL, which allow for efficient resource allocation and scalability. The research highlights the potential of heterogeneous architectures for real-time healthcare applications, including remote patient monitoring and telemedicine, providing a robust solution for future developments in non-invasive health technology.

关键词： real-time healthcare embedded systems heterogeneous computing deep learning CPU-GPU architecture

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real-time Noise Source Estimation of a Camera System from an image and Metadata

ADVANCED INTELLIGENT SYSTEMS

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ADVANCED INTELLIGENT SYSTEMS 2024年第6期6卷

作者： Wischow, Maik Irmisch, Patrick Boerner, Anko Gallego, Guillermo German Aerosp Ctr Inst Opt Sensor Syst Dept Real Time Data Proc D-12489 Berlin Germany Tech Univ Berlin Dept Elect Engn & Comp Sci D-10623 Berlin Germany Einstein Ctr Digital Future D-10117 Berlin Germany Sci Intelligence Excellence Cluster D-10587 Berlin Germany

Autonomous machines must self-maintain proper functionality to ensure the safety of humans and themselves. This pertains particularly to its cameras as predominant sensors to perceive the environment and support actions. A fundamental camera problem addressed in this study is noise. Solutions often focus on denoising images a posteriori, that is, fighting symptoms rather than root causes. However, tackling root causes requires identifying the noise sources, considering the limitations of mobile platforms. In this work, a real-time, memory-efficient, and reliable noise source estimator that combines data-based and physically based models is investigated. To this end, a deep neural network that examines an image with camera metadata for major camera noise sources is built and trained. In addition, it quantifies unexpected factors that impact image noise or metadata. This study investigates seven different estimators on six datasets that include synthetic noise, real-world noise from two camera systems, and real-field campaigns. For these, only the model with most metadata is capable to accurately and robustly quantify all individual noise contributions. This method outperforms total image noise estimators and can be plug-and-play deployed. It also serves as a basis to include more advanced noise sources, or as part of an automatic countermeasure feedback loop to approach fully reliable machines. images produced by cameras are corrupted by several types of noise that are internal to the camera. How can the different types and amount of noise be distinguished from an image and its camera metadata? A novel multibranch deep-learning neural network is proposed to solve this problem. Code is available to test on different camera models. image (c) 2024 WILEY-VCH GmbH

关键词： machine learnings noise estimations perceptions sensor artificial intelligences smart sensing systems (with image sensors)

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