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Infrared Fault Detection Method for Dense Electrolytic Bath Polar Plate Based on YOLOv5s

computers, Materials & Continua 2024年第9期80卷 4859-4874页

作者： Huiling Yu Yanqiu Hang Shen Shi Kangning Wu Yizhuo Zhang Software Engineering Department of Computer ScienceChangzhou UniversityChangzhou213146China Electrical Engineering Department of Computer ScienceChangzhou UniversityChangzhou213146China

Electrolysis tanks are used to smeltmetals based on electrochemical principles,and the short-circuiting of the pole plates in the tanks in the production process will lead to high temperatures,thus affecting normal *** at the problems of time-consuming and poor accuracy of existing infrared methods for high-temperature detection of dense pole plates in electrolysis tanks,an infrared dense pole plate anomalous target detection network YOLOv5-RMF based on You Only Look Once version 5(YOLOv5)is ***,we modified the Real-Time Enhanced Super-Resolution Generative Adversarial Network(Real-ESRGAN)by changing the U-shaped network(U-Net)to Attention U-Net,to preprocess the images;secondly,we propose a new Focus module that introduces the Marr operator,which can provide more boundary information for the network;again,because Complete Intersection over Union(CIOU)cannot accommodate target borders that are increasing and decreasing,replace CIOU with Extended Intersection over Union(EIOU),while the loss function is changed to Focal and Efficient IOU(Focal-EIOU)due to the different difficulty of sample *** the homemade dataset,the precision of our method is 94%,the recall is 70.8%,and the map@.5 is 83.6%,which is an improvement of 1.3%in precision,9.7%in recall,and 7%in map@.5 over the original *** algorithm can meet the needs of electrolysis tank pole plate abnormal temperature detection,which can lay a technical foundation for improving production efficiency and reducing production waste.

关键词： Infrared polar plate fault detection YOLOv5 Real-ESRGAN Marr boundary detection operator Focal-EIoU loss

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Object Detection Model for Remote Sensing Images Based on YOLOv9

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2025年第3期52卷 840-847页

作者： Hou, Donghao Zhang, Yujun School of Computer and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In the field of object detection for remote sensing images, especially in applications such as environmental monitoring and urban planning, significant progress has been made. This paper addresses the common challenges faced by traditional object detection methods in remote sensing images, such as the large number of targets and complex backgrounds, by proposing a novel network based on YOLOv9. The network innovatively introduces the C3_CD_CGA module, an enhanced module based on Cascaded Group Attention, designed to reduce computational redundancy and increase attention diversity, and enhances the processing capability of multi-scale information through the CD module. The C3 module employs deep asymmetric convolution to mitigate information loss and increase the receptive field. Additionally, the network integrates DSConv with the RepNCSPELAN4 module to adaptively focus on and precisely capture the features of elongated and curved local structures, such as vehicles. The introduction of the CARAFE module further improves the spatial resolution of the feature maps, significantly enhancing performance across various visual tasks. Experimental results show that the improved YOLOv9 achieves a mean average precision (mAP) of 88% on the SIMD dataset, which is an improvement of 1.6% compared to the baseline YOLOv9 model and 1.5% higher than the state-of-the-art YOLO-SE model. This model not only achieves more effective multi-target recognition in complex backgrounds but also strikes a good balance between accuracy and efficiency. © (2025), (International Association of Engineers). All rights reserved.

关键词： Urban planning

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Steel Surface Defect Detection Algorithm Based on S-YOLOv8

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2025年第3期52卷 644-652页

作者： Zhang, Xu Cui, Wenhua Tao, Ye Shi, Tianwei School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China

Steel, being a widely utilized material in industrial production, holds a pivotal role in ensuring product safety and longevity. Hence, the exploration and implementation of steel surface defect detection technology carry significant importance. This paper introduces a steel surface defect detection algorithm based on S-YOLOv8. The algorithm, rooted in YOLOv8n as a benchmark model, initially incorporates a shift-wise shift operator in the backbone network. This introduction notably enhances accuracy compared to conventional CNN models while markedly reducing computational demands. Furthermore, the utilization of the SF-Neck framework, integrating the scale sequence feature fusion module (SSFF) and triple feature encoder module (TFE) in the head network, enriches the network’s multi-scale information extraction capabilities. Subsequently, the adoption of the WIoU loss function enhances the overall detector performance. Lastly, the integration of the SEAM occlusion attention module refines the detection head segment of the YOLOv8 algorithm, effectively addressing defect occlusion challenges. Experiments conducted on the NEU-DET dataset reveal that the mAP value of the S-YOLOv8 model reaches an impressive 84.2%. Comparative analysis with other mainstream algorithms demonstrates a substantial enhancement in detection accuracy, alongside a reduction in instances of leakage and misdetection. Consequently, this study charts a new technical trajectory for quality control within the steel manufacturing industry. © (2025), (International Association of Engineers). All rights reserved.

关键词： Benchmarking

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An overview of computer—aided medical image classification

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Multimedia Tools and Applications 2025年第15期84卷 14489-14527页

作者： Boafo, Yaw Gyamerah College of Computer Science and Software Engineering Hohai University Jiangsu Nanjing211100 China

Medical image classification plays a pivotal role in modern healthcare, aiding in accurate disease diagnosis, treatment planning, and patient management. With the advent of deep learning techniques, significant advancements have been made in the field of medical image classification. Deep Learning (DL) has been proven to be a promising technique for image analysis tasks such as image classification. Compared with other fields, the accuracy of DL tasks in medical image classification depends heavily on the dataset volume. However, DL has been suffering from the problem of small sample datasets caused by a variety of ethical and financial reasons in medical image classification. Data augmentation and transfer learning are the two most commonly used approaches to enhance the practicability of the DL algorithms in medical imaging. This review provides an overview of some techniques, challenges, and some developments in medical image classification, with a focus on deep learning approaches. It also discusses the impact of medical image classification on clinical practice and suggests future research directions. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Clinical research

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A Scalable Unsupervised and Back Propagation Free Learning With SACSOM: A Novel Approach to SOM-Based Architectures

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 955-967页

作者： Hirani, Gaurav R. Wang, Kevin I-Kai Abdulla, Waleed H. The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand

The field of computer vision is predominantly driven by supervised models, which, despite their efficacy, are computationally expensive and often intractable for many applications. Recently, research has expedited alternative avenues such as self-organizing maps (SOM)-based architectures, which offer significant advantages such as tractability, the absence of back-propagation, and feed-forward unsupervised learning. However, these SOM-based approaches frequently suffer from lower accuracy and limited generalization capabilities. To address these shortcomings, we propose a novel model called split and concur SOM (SACSOM). SACSOM overcomes the limitations of closely related SOM-based algorithms by utilizing multiple parallel branches, each equipped with its own SOM modules that process data independently with varying patch sizes. Furthermore, by creating groups of classes and using respective training samples to train independent subbranches in each branch, our approach accommodates datasets with a large number of classes. SACSOM employs a simple yet effective labeling technique requiring minimal labeled samples. The outputs from each branch, filtered by a threshold, contribute to the final prediction. Experimental validation on MNIST-digit, Fashion-MNIST, CIFAR-10, and CIFAR-100 demonstrates that SACSOM achieves competitive accuracy with significantly reduced computation time. Furthermore, it exhibits superior performance and generalization capabilities, even in high-noise scenarios. The weights of the single-layered SACSOM provide meaningful insights into the patch-based learning pattern, enhancing its tractability and making it ideal from the perspective of explainable AI. This study addresses the limitations of current clustering techniques, such as K-means and traditional SOMs, by proposing a lightweight, manageable, and fast architecture that does not require a GPU, making it suitable for low-powered devices. © 2024 IEEE.

关键词： Unsupervised learning

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Multi-label, Classification-based Prediction of Breast Cancer Metastasis Directions

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2025年第1期52卷 1-10页

作者： Wang, Tingting Fan, Qi Tan, Liang Zhang, Beier School of Computer and Software Engineering Anhui Institute of Information Technology China School of Computer Science and Technology Huaibei Normal University China School of Computer and Software Engineering Anhui Institute of Information Technology China School of Computer Science and Technology Huaibei Normal University China

Predicting the metastatic direction of primary breast cancer (BC), thus assisting physicians in precise treatment, strict follow-up, and effectively improving the prognosis. The clinical data of 293,946 patients with primary BC diagnosed between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results database. Multiple interpolations and Multi-label Synthetic Minority Over-sampling Technique methods were used for data analysis, and machine learning model was established for multi-label classification. Finally, Surgical information, lymph node status, distant metastasis, tumor size, chemotherapy, histological type, and radiotherapy had significant influence as inputs. Compared with the k-nearest neighbor model, average accuracies of the decision tree and random forest (RF) models increased from 88.84% to 93.59% and 94.14%, respectively. Their average precision, recall rate, F1 score, area under the receiver operating characteristic curve and weighted-F1 increased from 87.24% to 95.85% and 94.74%, 87.73% to 90.40% and 91.76%, 87.07% to 92.16% and 93.45%, 97.11% to 99.53% and 99.95%, 82.13% to 89.44% and 90.48%, respectively. In conclusion, the RF model, which showed the best performance, can be used in multi-label prediction of BC metastasis directions, and can assist physicians in diagnosing and treating patients with primary BC. © (2025), (International Association of Engineers). All rights reserved.

关键词： Lung cancer

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Enhancing the adversarial robustness in medical image classification: exploring adversarial machine learning with vision transformers-based models

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Neural Computing and Applications 2025年第12期37卷 7971-7989页

作者： Kanca Gulsoy, Elif Ayas, Selen Baykal Kablan, Elif Ekinci, Murat Software Engineering Karadeniz Technical University Trabzon Turkey Computer Engineering Karadeniz Technical University Trabzon Turkey

Despite their remarkable achievements in computer-aided medical image-related tasks, including detection, segmentation, and classification, deep learning techniques remain vulnerable to imperceptible adversarial attacks, which could lead to potential misdiagnosis in clinical applications. Therefore, adversarial attacks and their defense in deep medical diagnosis systems have been witnessed remarkable progress in recent years. Although the importance of transformers in various medical applications has grown immensely, a critical concern for the reliability and security of their susceptibility to adversarial attacks has not yet been sufficiently investigated. Furthermore, many studies in the field of ViT-based adversarial machine learning mainly focus on pure ViT architecture. To this end, this paper provides a comprehensive evaluation, comparison, and analysis of state-of-the-art ViT-based models such as ViT, DeiT, Swin transformer, and PVTv2 for their robustness against FGSM and PGD adversarial machine learning attacks and investigates the impact of the k-step PGD adversarial training defense mechanism in the domain of various medical imaging tasks. The findings indicate that ViT-based models are vulnerable to adversarial attacks even at a small perturbation degree. Furthermore, the significant drop in accuracy from around 90.0% underlines the vulnerability of these models to adversarial attacks and highlights the urgent need for robust defenses. We also conclude that the ViT-based models show significant robustness against adversarial attacks using adversarial training, i.e., the defense strategy can achieve improved classification accuracy, close to the clean image accuracy. By analyzing quantitative results, we believe that this study aims to fill the gap in research on the robustness of ViT-based models to adversarial machine learning attacks in medical image analysis, highlighting future research directions. © The Author(s), under exclusive licence to Springer-

关键词： Adversarial machine learning

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Enhanced facial action unit detection with adaptable patch sizes on representative landmarks

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Neural Computing and Applications 2025年第5期37卷 3777-3791页

作者： Cakir, Duygu Yilmaz, Gorkem Arica, Nafiz Department of Software Engineering Bahcesehir University Istanbul Turkey Department of Computer Engineering Bahcesehir University Istanbul Turkey Department of Computer Engineering Piri Reis University Istanbul Turkey

The human face displays expressions through the contraction of various facial muscles. The Facial Action Coding System (FACS) is a widely accepted taxonomy that describes all visible changes in the face in terms of action units (AUs). In this study, AUs are examined by finding the most active landmarks of the face and then examining the most representative patch sizes of each landmark for the AU detection task. Sparse learning is employed to learn the most active landmarks for each AU, and then the active landmark patches are fed to ViT and Perceiver mechanisms independently. Experiments indicate that using active landmark patches with their most representative size improves the results when compared to using all the landmarks, especially when it is used on more challenging datasets as a support for the attention mechanism of the classifier. The results demonstrate that the proposed method improves the performance of the employed models and are further supported by experiments conducted across different datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Face recognition

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Building LOD representation for 3D urban scenes

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ISPRS Journal of Photogrammetry and Remote Sensing 2025年 226卷 16-32页

作者： Pan, Shanshan Zhang, Runze Liu, Yilin Gong, Minglun Huang, Hui College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Science University of Guelph Guelph Canada

The advances in 3D reconstruction technology, such as photogrammetry and LiDAR scanning, have made it easier to reconstruct accurate and detailed 3D models for urban scenes. Nevertheless, these reconstructed models often contain a large number of geometry primitives, making interactive manipulation and rendering challenging, especially on resource-constrained devices like virtual reality platforms. Therefore, the generation of appropriate levels-of-detail (LOD) representations for these models is crucial. Additionally, automatically reconstructed 3D models tend to suffer from noise and lack semantic information. Dealing with these issues and creating LOD representations that are robust against noise while capturing semantic meaning present significant challenges. In this paper, we propose a novel algorithm to address these challenges. We begin by analyzing the properties of planar primitives detected from the input and group these primitives into multiple level sets by forming meaningful 3D structures. These level sets form the nodes of our innovative LOD-Tree. By selecting nodes at appropriate depths within the LOD-Tree, different LOD representations can be generated. Experimental results on real and complex urban scenes demonstrate the merits of our approach in generating clean, accurate, and semantically meaningful LOD representations. © 2025

关键词： Three dimensional computer graphics

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Semi-Supervised Skin Lesion Segmentation Based on Pseudo-Labels

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2025年第2期52卷 325-332页

作者： Mu, Bo Wei, JingXin Zhang, Yujun School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In recent years, deep learning has significantly advanced skin lesion segmentation. However, annotating medical image data is specialized and costly, while obtaining unlabeled medical data is easier. To address this challenge, we propose a semi-supervised learning strategy to improve segmentation accuracy by combining a small amount of annotated data with a larger volume of unlabeled data. Our approach employs a teacher-student model framework. In this framework, the teacher model generates pseudo-labels for the unlabeled data, and the student model is trained using both these pseudo-labels and the limited true labels. To improve the student model’s learning capacity, we introduce auxiliary segmentation heads that provide joint guidance during training. We use the crossentropy (CE) loss function to quantify the discrepancies between the segmentation outputs of the main head and auxiliary heads. Since pseudo-labels generated by the teacher model may contain noise, we developed a mechanism to identify and exclude uncertain regions in each unlabeled image. This reduces pseudolabel noise and mitigates its negative impact on the student model. Our method demonstrates significant improvements in skin lesion segmentation on the publicly available ISIC2018 dataset, achieving Dice coefficients of 87.84% and 88.73% with only 5% and 10% of the total annotated data, respectively, outperforming existing methods. © (2025), (International Association of Engineers). All rights reserved.

关键词： Self-supervised learning

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