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Weld Pool Boundary Detection Based on the U-Net Algorithm and Weld Seam Tracking in Plasma Arc Welding

APPLIED SCIENCES-BASEL 2025年第4期15卷

作者： Lu, Jidong Yamane, Satoshi Wang, Weixi Li, Ning Wang, Siqi Xia, Yuxiong Saitama Univ Grad Sch Sci & Engn Saitama 3388570 Japan Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 210044 Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China

Plasma arc welding can achieve high-quality welding joints in high-strength manufacturing fields, such as aviation and automotive, and improve production efficiency. It is important to observe the weld pool state in real-time in robot automatic welding. However, the electrodes of the plasma welding torch cannot be observed from the outside. Teaching the weld line to torch in real-time to be observable to humans will be difficult. Also, it is difficult to process the image to obtain the position of the weld line in K-PAW. In this study, a camera was utilized to observe the weld pool. The authors estimate the weld line position in real time by image processing based on U-Net prediction. The U-Net model demonstrates sufficient prediction where the accuracy reached 99.5% for the training data and 96.5% for the test data recognition. Moreover, a control method utilized weld line position estimated from the boundary area to verify the effectiveness of this prediction model from 3 mm within the deviation of 1 mm, which is within the range of permissible welding errors. It could reduce image processing errors in the weld pool image and provide higher recognition accuracy than image processing. Combining vision sensing technologies and deep learning methods will provide new technologies to enable higher welding precision and improve welding quality. It could also accelerate the development of welding technology in the intelligent manufacturing field.

关键词： plasma arc welding U-Net image processing adaptive control deep learning

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Research on rail surface crack identification and detection based on deep learning and image processing

Research on rail surface crack identification and detection ...

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3D Immersion, Interaction and Multi-sensory Experiences (ICDIIME), International Conference on

作者： Wang Yu Miaobing Rong Daihu Min Zhang Ying Zhaoyi Guang State Key Laboratory of Traction Power Southwest Jiaotong University Chengdu Sichuan China College of Advanced Manufacturing Technology Guangdong Institute of Mechanical and Electrical Technology Guangzhou China

With the implementation of the development program of our country’s transportation country, the traffic construction has welcomed a booming development, the passenger transportation and freight transportation increasing year by year, but it brings more and more high requirements on the quality of the steel rail, the steel rail due to the long-term service and all kinds of defects appear, especially with the rolling contact fatigue cracks. A method and system of rail surface crack identification and detection based on deep learning is proposed. The crack features can be accurately extracted from the complex image of rail surface for real-time crack identification and detection. The crack width can be identified as 0.1mm, and the recognition speed can reach 1-2ms. The rapid identification and detection of rail surface cracks are completed.

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An improved pulse coupled neural networks model for semantic IoT

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Digital Communications and Networks 2024年第3期10卷 557-567页

作者： Rong Ma Zhen Zhang Yide Ma Xiping Hu Edith C.H.Ngai Victor C.M.Leung School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China School of Medical Technology Beijing Institute of TechnologyBeijing100081China Department of Electrical and Electronic Engineer The University of Hong KongHong Kong 999077China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Electrical and Computer Engineering the University of British ColumbiaVancouverBC V6T 1Z4Canada

In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data ***,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication ***,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic *** the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to *** contrast,the Pulse Coupled Neural Network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image ***,the parameters of PCNN are determined by trial and error,which limits its *** overcome this limitation,an Improved Pulse Coupled Neural Networks(IPCNN)model is proposed in this *** IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of *** segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation *** IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.

关键词： Internet of things(IoT) Semantic information real-time application Improved pulse coupled neural network image segmentation

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deep learning for Traffic Scene Understanding: A Review

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IEEE ACCESS 2025年 13卷 13187-13237页

作者： Dolatyabi, Parya Regan, Jacob Khodayar, Mahdi Univ Tulsa Dept Comp Sci Tulsa OK 74104 USA

This review paper presents an in-depth analysis of deep learning (DL) models applied to traffic scene understanding, a key aspect of modern intelligent transportation systems. It examines fundamental techniques such as classification, object detection, and segmentation, and extends to more advanced applications like action recognition, object tracking, path prediction, scene generation and retrieval, anomaly detection, image-to-image Translation (I2IT), and person re-identification (Person Re-ID). The paper synthesizes insights from a broad range of studies, tracing the evolution from traditional image processing methods to sophisticated DL techniques, such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). The review also explores three primary categories of domain adaptation (DA) methods: clustering-based, discrepancy-based, and adversarial-based, highlighting their significance in traffic scene understanding. The significance of Hyperparameter Optimization (HPO) is also discussed, emphasizing its critical role in enhancing model performance and efficiency, particularly in adapting DL models for practical, real-world use. Special focus is given to the integration of these models in real-world applications, including autonomous driving, traffic management, and pedestrian safety. The review also addresses key challenges in traffic scene understanding, such as occlusions, the dynamic nature of urban traffic, and environmental complexities like varying weather and lighting conditions. By critically analyzing current technologies, the paper identifies limitations in existing research and proposes areas for future exploration. It underscores the need for improved interpretability, real-time processing, and the integration of multi-modal data. This review serves as a valuable resource for researchers and practitioners aiming to apply or advance DL techniques in traffic scene understanding.

关键词： Reviews Adaptation models Object detection deep learning real-time systems Accuracy Feature extraction Synthetic data Data models Autonomous vehicles traffic scene understanding discriminative models generative models domain adaptation classification object detection segmentation

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High Precision Robust real-time Lightweight Approach for Railway Pantograph Slider Wear Estimation

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第5期25卷 3973-3985页

作者： Tang, Qingfeng Wei, Xiukun Wei, Dehua Shen, Xing Yin, Xinqiang Wang, Diqing Jia, Limin Zhong, Qitian Beijing Jiaotong Univ State Key Lab Adv Rail Autonomous Operat Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China

The pantograph slider is a key component of the pantograph-catenary system. It is important to monitor the wear of sliders for rail transit safety. In this paper, an innovative real-time high-precision lightweight approach is proposed to estimate the wear of the slider. It allows complete monitoring of all sliders of the pantograph. In the first stage, a method based on image processing and object detection by deep learning is proposed to locate the region of the slider. It takes into account the large aspect ratio on the pantograph slider and the inclined angle. In the second stage, the neural network for wear estimation of pantograph slider (WEPSNet) is proposed. It realizes end-to-end contour extraction of the slider. The residual thickness of the slider is calculated by counting the number of pixels and the error is analyzed. Furthermore, the error arising from the perspective projection transformation in the monocular image is discussed. The experimental results demonstrate that, with the similar model size, the proposed WEPSNet outperforms the state-of-the-art method by 1.08% mIoU and 4.63% IMP. Moreover, the accuracy of residual thickness is tested on 120 pantograph slider images, achieving up to 95.91% within the allowable 1mm error, which is 6.68% higher than the state-of-the-art method.

关键词： image processing real-time systems Monitoring image edge detection deep learning Head Rails Pantograph slider thickness estimation WEPSNet contour extraction deep learning

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A Hardware Accelerator for real-time processing Platforms Used in Synthetic Aperture Radar Target Detection Tasks

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MICROMACHINES 2025年第2期16卷 193页

作者： Zhang, Yue Tang, Yunshan Cao, Yue Yu, Zhongjun Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 101408 Peoples R China

The deep learning object detection algorithm has been widely applied in the field of synthetic aperture radar (SAR). By utilizing deep convolutional neural networks (CNNs) and other techniques, these algorithms can effectively identify and locate targets in SAR images, thereby improving the accuracy and efficiency of detection. In recent years, achieving real-time monitoring of regions has become a pressing need, leading to the direct completion of real-time SAR image target detection on airborne or satellite-borne real-time processing platforms. However, current GPU-based real-time processing platforms struggle to meet the power consumption requirements of airborne or satellite applications. To address this issue, a low-power, low-latency deep learning SAR object detection algorithm accelerator was designed in this study to enable real-time target detection on airborne and satellite SAR platforms. This accelerator proposes a Process Engine (PE) suitable for multidimensional convolution parallel computing, making full use of Field-Programmable Gate Array (FPGA) computing resources to reduce convolution computing time. Furthermore, a unique memory arrangement design based on this PE aims to enhance memory read/write efficiency while applying dataflow patterns suitable for FPGA computing to the accelerator to reduce computation latency. Our experimental results demonstrate that deploying the SAR object detection algorithm based on Yolov5s on this accelerator design, mounted on a Virtex 7 690t chip, consumes only 7 watts of dynamic power, achieving the capability to detect 52.19 512 x 512-sized SAR images per second.

关键词： synthetic aperture radar (SAR) imagery convolutional neural networks (CNNs) target detection tasks

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An Affordable Intelligent Navigation Backpack for the Visually Impaired: deep learning-Based Obstacle Detection and real-time Navigation with RGB-D Integration

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JOURNAL OF INTERNET TECHNOLOGY 2025年第2期26卷 265-271页

作者： Luo, Cheng-Li Xu, Hai Liu, Min Chu, Shu-Chuan Yango Univ Sch Informat Engn Mawei Peoples R China Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing Peoples R China

According to the World Health Organization (WHO), approximately 285 million people worldwide suffer from some form of visual impairment, including 39 million who are blind and an additional 246 million experiencing severe visual impairment. Existing navigation aids often fail to provide a user-centric perspective, relying on secondary judgment and leading to inconvenience. Hightech devices, such as smart glasses and robots, offer more effective solutions but are frequently cost-prohibitive. This study presents an affordable, first-person perspective intelligent navigation backpack for the visually impaired, utilizing deep learning. The system integrates RGB images and depth maps via alignment algorithms, extracts obstacle contours through binary image processing, and detects obstacles in real-time using the YOLO model. Experimental results demonstrate that the navigation depth camera significantly outperforms traditional ultrasonic and LiDAR sensors, achieving up to 98% measurement accuracy.

关键词： Assistive navigation system image recognition deep learning image morphology

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deep learning Model-Based real-time Inspection System for Foreign Particles inside Flexible Fluid Bags

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APPLIED SCIENCES-BASEL 2024年第17期14卷 7960页

作者： Lim, Chae Whan Son, Kwang Chul Shinhan Univ Microdegree Coll 95 Hoam Ro Uijeongbu Si 11644 South Korea Kwangwoon Univ Dept Smart Elect & Elect 20 Gwangun Ro Seoul 01897 South Korea

Intravenous fluid bags are essential in hospitals, but foreign particles can contaminate them during mass production, posing significant risks. Although produced in sanitary environments, contamination can cause severe problems if products reach consumers. Traditional inspection methods struggle with the flexible nature of these bags, which deform easily, complicating particle detection. Recent deep learning advancements offer promising solutions in regard to quality inspection, but high-resolution image processing remains challenging. This paper introduces a real-time deep learning-based inspection system addressing bag deformation and memory constraints for high-resolution images. The system uses object-level background rejection, filtering out objects similar to the background to isolate moving foreign particles. To further enhance performance, the method aggregates object patches, reducing unnecessary data and preserving spatial resolution for accurate detection. During aggregation, candidate objects are tracked across frames, forming tracks re-identified as bubbles or particles by the deep learning model. Ensemble detection results provide robust final decisions. Experiments demonstrate that this system effectively detects particles in real-time with over 98% accuracy, leveraging deep learning advancements to tackle the complexities of inspecting flexible fluid bags.

关键词： flexible fluid bags object-level background rejection deep learning model real-time inspection aggregation of object patches object tracking

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A Dilated MultiRes Visual Attention U-Net for historical document image binarization

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SIGNAL processing-image COMMUNICATION 2024年 122卷

作者： Detsikas, Nikolaos Mitianoudis, Nikolaos Papamarkos, Nikolaos Democritus Univ Thrace Elect & Comp Engn Dept Univ Campus Xanthi Kimmeria Xanthi 67100 Greece

The task of binarization of historical document images has been in the forefront of image processing research, during the digital transition of libraries. The process of storing and transcribing valuable historical printed or handwritten material can salvage world cultural heritage and make it available online without physical attendance. The task of binarization can be viewed as a pre-processing step that attempts to separate the printed/handwritten characters in the image from possible noise and stains, which will assist in the Optical Character Recognition (OCR) process. Many approaches have been proposed before, including deep learning based approaches. In this article, we propose a U -Net style deep learning architecture that incorporates many other developments of deep learning, including residual connections, multi -resolution connections, visual attention blocks and dilated convolution blocks for upsampling. The novelties in the proposed DMVAnet lie in the use of these elements in combination in a novel U -Net style architecture and the application of DMVAnet in image binarization for the first time. In addition, the proposed DMVAnet is a very computationally lightweight network that performs very close or even better than the state-of-the-art approaches with a fraction of the network size and parameters. Finally, it can be used on platforms with restricted processing power and system resources, such as mobile devices and through scaling can result in inference times that allow for real-time applications.

关键词： Semantic segmentation Historical document image binarization Attention U-Net architectures Residual connections

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An ensemble deep learning model for fast classification of Twitter spam

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INFORMATION & MANAGEMENT 2024年第8期61卷

作者： Dhar, Suparna Bose, Indranil NSHM Knowledge Campus 12460Basanta Lal Saha RdT Kolkata 700053 West Bengal India NEOMA Business Sch 59 rue Pierre Taittinger F-51100 Reims France

Twitter spam needs to be detected and arrested quickly. The paper examines methods for classification of spam in terms of determination of important features, comparative performance of classification models, and improvement in time performance for classification. It presents a conceptualization of several novel rich, deep, and na & iuml;ve features. The extraction processes for rich and deep features increase the time complexity of spam classification. To address this, the proposed model selectively segregates and combines features to enable near real-time processing. This supersedes the time performance of standard machine learning and deep learning models, with no compromise on the quality of classification.

关键词： deep learning ensemble models image analytics Spam classification Text analytics Twitter

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