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Conference on real-time image processing and deep learning

作者： Lee, Seungeon Kim, Donyung Kim, Sungho Yeungnam Univ Dept Elect Engn Gyeongbuk 38541 South Korea

ISBN: (纸本)9781510673878;9781510673861

In this paper, we proposed a template matching technique using deep learning to match pairs of wide fields of view and narrow field of view infrared images. The deep learning network has a similar structure with the Atrous Spatial Pyramid Pooling (ASPP) module and both wide and narrow fields of view images are input to the same network, so the network weights are shared. Our experiments used the Galaxy S20 (Qualcomm Snapdragon 865) platform and show that the trained network has higher matching accuracy than other template matching techniques and is fast enough to be used in real time.

关键词： template matching infrared image deep-learning real-time CNN Snapdragon 865

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real-time Motion Analysis With 4D deep learning for Ultrasound-Guided Radiotherapy

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2023年第9期70卷 2690-2699页

作者： Bengs, Marcel Sprenger, Johanna Gerlach, Stefan Neidhardt, Maximilian Schlaefer, Alexander Hamburg Univ Technol Inst Med Technol & Intelligent Syst D-21073 Hamburg Germany

Motion compensation in radiation therapy is a challenging scenario that requires estimating and forecasting motion of tissue structures to deliver the target dose. Ultrasound offers direct imaging of tissue in real-time and is considered for image guidance in radiation therapy. Recently, fast volumetric ultrasound has gained traction, but motion analysis with such high-dimensional data remains difficult. While deep learning could bring many advantages, such as fast data processing and high performance, it remains unclear how to process sequences of hundreds of image volumes efficiently and effectively. We present a 4D deep learning approach for real-time motion estimation and forecasting using long-term 4D ultrasound data. Using motion traces acquired during radiation therapy combined with various tissue types, our results demonstrate that long-term motion estimation can be performed markerless with a tracking error of 0.35 +/- 0.2 mm and with an inference time of less than 5 ms. Also, we demonstrate forecasting directly from the image data up to 900 ms into the future. Overall, our findings highlight that 4D deep learning is a promising approach for motion analysis during radiotherapy.

关键词： 4D deep learning sequence processing ultrasound motion analysis radiation therapy

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Online Recognition of Manually Spray-Printed Ladle Numbers in Complex Dynamic Environments

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Meng, Hongji Ma, Hancong Yang, Jian Hu, Zhenwei Fan, Jia He, Qing Northeastern Univ Coll Informat Sci & Engn Dept Intelligent Detect Shenyang 110819 Peoples R China HBIS Grp HanSteel Co Handan Peoples R China

During the converter process, it is crucial to automatically identify and record ladle numbers to track steel product quality and enhance automation levels. However, the steelmaking environment presents several challenges, including intricate ladle scheduling, varying lighting conditions, severe background interference, and significant disparities between manually spray-printed ladle number characteristics and publicly available datasets. The combination of these problems makes it challenging to perform accurate and real-time ladle number identification. In response, this article suggests an automatic ladle number recognition approach based on deep learning and image processing. First, a double-region object detection model based on YOLOv5 is employed to capture keyframe images of the ladle to be identified from the video stream. Then, a method that can enable the acquisition of an accurate region of ladle numbers in sophisticated industrial settings is proposed to address the distortion of numerical features caused by lighting variations and background interference in industrial environments. Last, leveraging the proprietary dataset found and a ladle number recognition model integrating CNN and multiframe image fusion is designed, developing multithreading design and image queue management to ensure real-time and accurate ladle number recognition. In this study, the video data of a steel plant is used for testing. Through testing 176 steelmaking production cycles, all ladle numbers are accurately identified prior to finishing charging molten iron, indicating the high accuracy and real-time capability of the recognition system.

关键词： image recognition Feature extraction YOLO real-time systems Streaming media Iron image segmentation Complex dynamic environments deep learning image processing ladle number recognition online recognition

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Enhanced image processing for Automobile Accident Detection Using deep learning

Enhanced Image Processing for Automobile Accident Detection ...

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Smart Technologies and Systems for Next Generation Computing (ICSTSN), International Conference on

作者： S. Usharani J. Dhanushkumar P. Manju Bala A. Balachandar Department of Artificial Intelligence and Machine Learning IFET College of Engineering (An Autonomous Institution) Villupuram India Department of Computer Science and Engineering IFET College of Engineering (An Autonomous Institution) Villupuram India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391572

Road safety can be creatively increased by utilizing systems for reporting and detecting accidents use the YOLO algorithm. Yolo, which stands for "You Only Look Once," is a sophisticated object recognition system that can identify and pinpoint objects in live video streams. By identifying accidents and notifying emergency services, the system lowers the reaction times and increases the possibility of saving lives by utilizing the YOLO algorithm. The object detection and alarm systems are the two primary parts of the proposed system. The YOLO method is employed by the object recognition module to look for accidents in live video broadcasts. A module of accident photos was used to teach the system to accurately identify incidents. When an accident is discovered, an alert system is activated. The location of the accident and a brief account of what transpired are communicated to the emergency services by the alert system. This information is communicated to emergency services through a wireless communication network, which expedites response times and increases the likelihood of saving lives. Positive results were obtained by testing the system using the images and an accident module. It was shown that the warning system could react in a couple of seconds and that the YOLO algorithm could identify accidents having a precision of around 94. Highways, busy intersections, and other high-risk areas may have systems in place to increase traffic safety and lower the number of accidents. Developing crash detection and warning systems using deep learning using the YOLO technique is one way to potentially increase road safety. With the use of technology, problems may be precisely identified in real-time video feeds, alerting emergency services and potentially increasing survival rates and reaction times.

关键词： YOLO deep learning Accuracy Alarm systems Streaming media Emergency services Road safety

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Development of an Intelligent Weld Surface Defect Detection System: Design and Application

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IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE 2025年第3期28卷 12-21页

作者： Wang, Guan-Qiang Zeng, He Chen, Ming-Song Lin, Yong-Cheng Zhang, Chi-Zhou Tan, Xian-Hua Guilin Univ Elect Sci & Technol Sch Mech & Elect Engn Guilin Peoples R China Natl Univ Def Technol Sch Intelligent Sci Changsha Peoples R China Cent South Univ Changsha Peoples R China Cent South Univ Light Alloy Res Inst Changsha Peoples R China Cent South Univ Sch Mech & Elect Engn Changsha Peoples R China Cent South Univ SLAM Deep Learning & Multisensory Fus Technol Nav Changsha Peoples R China

Early evaluation through the detection of surface defects in welds is essential to ensure the quality of weldments. In this study, considering the complexity of weld surface inspection, we designed a high-performance inspection system that combines image processing, defect detection, and model evolution functions. This system facilitates the engineering application of image super-resolution reconstruction, defect detection, and semi-supervised learning methods in real-world weld detection scenarios through the integrated weld surface inspection module and a visually interactive client. The experiment results demonstrate that the system can perform real-time processing, with a speed surpassing 30 frames per second, for super-resolution and detection evaluation of input images. Furthermore, it has autonomous learning capabilities, which continuously improve detection precision and reduce the miss rate during long-time detection.

关键词： Surface reconstruction Welding Superresolution Inspection Semisupervised learning real-time systems Surface treatment System analysis and design image reconstruction Defect detection Defect detection

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Catalysing assistive solutions by deploying light-weight deep learning model on edge devices

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2025年第3期37卷 465-486页

作者： Manjari, Kanak Verma, Madhushi Singal, Gaurav Chamola, Vinay Bennett Univ Dept Comp Sci Engn Greater Noida India NSUT Dept Comp Sci & Engn Delhi India BITS Dept Elect & Elect Engn Pilani India

Nowadays, real-time object detection, which is a crucial task, is being performed through image processing and deep learning techniques. As there are several high-performance computing edge devices available, selecting the best-fit device for a particular problem is a tough task and keeping in mind the cost, performance, and weight of the device in mind. One faces several challenges while performing this task in real-time such as a lack of resources in terms of power and mobility. We have provided an insight into the computation power of devices in terms of Frames per Second (FPS) by deploying object detection models on them. This paper will provide insight into selecting the appropriate combination of device and object detection models for real-time applications. Raspberry Pi 3 (RPi3), Raspberry Pi 4 (RPi4), Intel Neural Compute Stick 2 (NCS2), and Nvidia Jetson NANO are popular devices with high computation power used for real-time applications. The memory constraints of devices along with the deployment of different You Only Look Once (YOLO) and Single-Shot Detector (SSD) are the two object detection models that have been explained in this paper. A deep learning inference optimiser, TensorRT, has been used in NANO to achieve high throughput in the performance of object detection. The precision, recall, and F1 score achieved on deploying each tested model have been presented. After observing the devices during experimentation, RPi4+NCS2 showed the best execution with the blend of factors i.e. speed, portability, and user-friendliness.

关键词： Assistive technology edge devices raspberry neural compute stick jetson NANO

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Integrating YOLOv8 and CSPBottleneck based CNN for enhanced license plate character recognition

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JOURNAL OF real-time image processing 2024年第5期21卷 168页

作者： Khokhar, Sahil Kedia, deepak GJUS&T Dept ECE Hisar India

The paper introduces an integrated methodology for license plate character recognition, combining YOLOv8 for segmentation and a CSPBottleneck-based CNN classifier for character recognition. The proposed approach incorporates pre-processing techniques to enhance the recognition of partial plates and augmentation methods to address challenges arising from colour diversity. Performance analysis demonstrates YOLOv8's high segmentation accuracy and fast processing time, complemented by precise character recognition and efficient processing by the CNN classifier. The integrated system achieves an overall accuracy of 99.02% with a total processing time of 9.9 ms, offering a robust solution for automated license plate recognition (ALPR) systems. The integrated approach presented in the paper holds promise for the practical implementation of ALPR technology and further development in the field of license plate recognition systems.

关键词： Computer vision Optical character recognition deep learning Artificial intelligence ALPR

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real-time diabetic foot ulcer classification based on deep learning & parallel hardware computational tools

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MULtimeDIA TOOLS AND APPLICATIONS 2024年第27期83卷 70369-70394页

作者： Fadhel, Mohammed A. Alzubaidi, Laith Gu, Yuantong Santamaria, Jose Duan, Ye Univ Sumer Coll Comp Sci & Informat Technol Thi Qar 64005 Iraq Queensland Univ Technol Sch Mech Med & Proc Engn Brisbane Qld 4000 Australia Queensland Univ Technol ARC Ind Transformat Training Ctr Joint Biomech Brisbane Qld 4000 Australia Akunah Med Technol Pty Ltd Co Brisbane Qld 4120 Australia Univ Jaen Dept Comp Sci Jaen 23071 Spain Clemson Univ Sch Comp Clemson SC 29631 USA

Meeting the rising global demand for healthcare diagnostic tools is crucial, especially with a shortage of medical professionals. This issue has increased interest in utilizing deep learning (DL) and telemedicine technologies. DL, a branch of artificial intelligence, has progressed due to advancements in digital technology and data availability and has proven to be effective in solving previously challenging learning problems. Convolutional neural networks (CNNs) show potential in image detection and recognition, particularly in healthcare applications. However, due to their resource-intensiveness, they surpass the capabilities of general-purpose CPUs. Therefore, hardware accelerators such as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), and graphics processing units (GPUs) have been developed. With their parallelism efficiency and energy-saving capabilities, FPGAs have gained popularity for DL networks. This research aims to automate the classification of normal and abnormal (specifically Diabetic Foot Ulcer-DFU) classes using various parallel hardware accelerators. The study introduces two CNN models, namely DFU_FNet and DFU_TFNet. DFU_FNet is a simple model that extracts features used to train classifiers like SVM and KNN. On the other hand, DFU_TFNet is a deeper model that employs transfer learning to test hardware efficiency on both shallow and deep models. DFU_TFNet has outperformed AlexNet, VGG16, and GoogleNet benchmarks with an accuracy 99.81%, precision 99.38% and F1-Score 99.25%. In addition, the study evaluated two high-performance computing platforms, GPUs and FPGAs, for real-time system requirements. The comparison of processing time and power consumption revealed that while GPUs outpace FPGAs in processing speed, FPGAs exhibit significantly lower power consumption than GPUs.

关键词： deep learning Double transfer learning FPGA GPU DFU real-time Parallel hardware Medical imaging

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deep learning-Based Optimization of Cloud Enterprise Resource Planning (ERP) Systems for Adaptive Decision Support and Management Effectiveness Analysis

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IEEE ACCESS 2024年 12卷 193402-193415页

作者： Zhang, Li-Sen George Washington Univ Sch Business Washington DC 20052 USA

Modern enterprise resource planning (ERP) systems face the challenge of handling massive amounts of data and supporting real-time decision-making. With the rapid changes in the market environment, traditional ERP systems are limited in their ability to make adaptive decisions. This study aims to address this issue by integrating deep learning techniques to enhance the management effectiveness of ERP systems. The study uses RNNs, CNNs and DRL models for time series prediction, image recognition and resource optimisation, respectively. The experimental results show that RNN achieves 95% accuracy in demand forecasting, CNN 98% accuracy in image recognition, and DRL achieves more than 10% cost savings in resource optimisation. The integrated ERP system achieved a 42.86% reduction in order processing time, a 25% improvement in inventory turnover, an 8% reduction in operating costs, and a 15% improvement in employee satisfaction. This study demonstrates the effectiveness of deep learning to enhance decision support in ERP systems and provides suggestions for future directions of improvement.

关键词： Optimization Accuracy Convolutional neural networks Adaptive systems Decision making Business Recurrent neural networks Predictive models Enterprise resource planning Adaptation models Thesaurus deep learning cloud-based enterprise resource planning system adaptive decision support management effectiveness

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Perceptible Lightweight Zero-Mean Normalized Cross-Correlation for Infrared Template Matching

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IEEE ACCESS 2024年 12卷 164777-164791页

作者： Lee, Seungeon Kim, Donyung Park, Inho Kim, Geonjong Kim, Sungho Yeungnam Univ Dept Elect Engn Gyongsan 38541 Gyeongsangbug D South Korea Hanwha Syst Seoul 04541 South Korea

Infrared template matching is an essential technology that enables reliable and accurate object detection, recognition, and tracking in complex environments. Perceptible Lightweight Zero-mean normalized cross-correlation (ZNCC) Template Matching (PLZ-TM) has been proposed as a tool for matching infrared images obtained from cameras with different fields of view. Aligning such images is challenging because of the involved differences in thermal distributions, focus discrepancies, background elements, and distortions. The first stage of PLZ-TM involves extracting feature maps from the search and template images using a deep learning network. This deep learning network is designed with a Convolutional Neural Network (CNN) architecture that omits pooling layers, thereby minimizing information loss during extraction. The subsequent stage involves matching the feature maps. The matching method utilizes a lightweight ZNCC (ZNCC) module that employs average pooling for training. The deep learning network is trained to optimize the distribution of the output heatmap and the probability at the correct location of the template image. PLZ-TM delivers excellent performance achieving a processing time of only 3.3 ms in matching a $640\times 480$ search image with a $192\times 144$ template image. Moreover, it attains a matching accuracy of 96% on a dataset obtained from infrared cameras with different fields of view.

关键词： Feature extraction deep learning image matching Accuracy Training Cameras real-time systems Brightness Object tracking Convolutional neural networks Infrared imaging Template matching zero-mean normalized cross correlation real-time infrared image matching convolutional neural network

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