Chen-Chiung Hsieh^1,*, Chun-An Chen¹, Wei-Hsin Huang²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072514 - 12 January 2026

Abstract Container transportation is pivotal in global trade due to its efficiency, safety, and cost-effectiveness. However, structural defects—particularly in grapple slots—can result in cargo damage, financial loss, and elevated safety risks, including container drops during lifting operations. Timely and accurate inspection before and after transit is therefore essential. Traditional inspection methods rely heavily on manual observation of internal and external surfaces, which are time-consuming, resource-intensive, and prone to subjective errors. Container roofs pose additional challenges due to limited visibility, while grapple slots are especially vulnerable to wear from frequent use. This study proposes a two-stage automated… More >

Yuanyuan Wang^1,*, Yemeng Zhu¹, Xiuchuan Chen¹, Tongtong Yin¹, Shiwei Su²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072404 - 12 January 2026

Abstract To solve the false detection and missed detection problems caused by various types and sizes of defects in the detection of steel surface defects, similar defects and background features, and similarities between different defects, this paper proposes a lightweight detection model named multiscale edge and squeeze-and-excitation attention detection network (MSESE), which is built upon the You Only Look Once version 11 nano (YOLOv11n). To address the difficulty of locating defect edges, we first propose an edge enhancement module (EEM), apply it to the process of multiscale feature extraction, and then propose a multiscale edge enhancement… More >

Md. Ehsanul Haque¹, Md. Nurul Absur², Fahmid Al Farid³, Md Kamrul Siam⁴, Jia Uddin^5,*, Hezerul Abdul Karim^3,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071771 - 12 January 2026

Abstract Manual inspection of onba earing casting defects is not realistic and unreliable, particularly in the case of some micro-level anomalies which lead to major defects on a large scale. To address these challenges, we propose BearFusionNet, an attention-based deep learning architecture with multi-stream, which merges both DenseNet201 and MobileNetV2 for feature extraction with a classification head inspired by VGG19. This hybrid design, figuratively beaming from one layer to another, extracts the enormity of representations on different scales, backed by a pre-preprocessing pipeline that brings defect saliency to the fore through contrast adjustment, denoising, and edge… More >

Tao Geng¹, Shuaibing Li^1,*, Yunyun Yun¹, Yongqiang Kang¹, Hongwei Li², Junmin Zhu²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071644 - 12 January 2026

Abstract In order to address the challenges posed by complex background interference, high miss-detection rates of micro-scale defects, and limited model deployment efficiency in photovoltaic (PV) module defect detection, this paper proposes an efficient detection framework based on an improved YOLOv11 architecture. First, a Re-parameterized Convolution (RepConv) module is integrated into the backbone to enhance the model’s sensitivity to fine-grained defects—such as micro-cracks and hot spots—while maintaining high inference efficiency. Second, a Multi-Scale Feature Fusion Convolutional Block Attention Mechanism (MSFF-CBAM) is designed to guide the network toward critical defect regions by jointly modeling channel-wise and spatial… More >

Zhuangqiang Wen¹, Min Zhang², Zhekun Shou^3,*

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.071300 - 08 January 2026

Abstract Roadbed disease detection is essential for maintaining road functionality. Ground penetrating radar (GPR) enables non-destructive detection without drilling. However, current identification often relies on manual inspection, which requires extensive experience, suffers from low efficiency, and is highly subjective. As the results are presented as radar images, image processing methods can be applied for fast and objective identification. Deep learning-based approaches now offer a robust solution for automated roadbed disease detection. This study proposes an enhanced Faster Region-based Convolutional Neural Networks (R-CNN) framework integrating ResNet-50 as the backbone and two-dimensional discrete Fourier spectrum transformation (2D-DFT) for… More >

Jianhua Du¹, Shaofeng Wang¹, Ting Gao², Huiwen Sun², Wenjing Liu^1,*

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.071189 - 08 January 2026

Abstract In ultrasonic non-destructive testing of high-temperature industrial equipment, sound velocity drift induced by non-uniform temperature fields can severely compromise defect localization accuracy. Conventional approaches that rely on room-temperature sound velocities introduce systematic errors, potentially leading to misjudgment of safety-critical components. Two primary challenges hinder current methods: first, it is difficult to monitor real-time changes in sound velocity distribution within a thermal gradient; second, traditional uniform-temperature correction models fail to capture the nonlinear dependence of material properties on temperature and their effect on ultrasonic velocity fields. Here, we propose a defect localization correction method based on… More >

Hua-Qin Wu^1,2, Hao Yan^1,2, Hong Zhang^1,2,*, Shun-Wu Xu^1,2, Feng-Yu Gao^1,2, Zhao-Wen Chen^1,2

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.070589 - 08 January 2026

Abstract In industrial manufacturing, efficient surface defect detection is crucial for ensuring product quality and production safety. Traditional inspection methods are often slow, subjective, and prone to errors, while classical machine vision techniques struggle with complex backgrounds and small defects. To address these challenges, this study proposes an improved YOLOv11 model for detecting defects on hot-rolled steel strips using the NEU-DET dataset. Three key improvements are introduced in the proposed model. First, a lightweight Guided Attention Feature Module (GAFM) is incorporated to enhance multi-scale feature fusion, allowing the model to better capture and integrate semantic and… More >

Hongcheng Zhao¹, Tong Yang ², Yihui Hu², Fengxiang Guo^2,*

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.068987 - 08 January 2026

Abstract With the rapid development of transportation infrastructure, ensuring road safety through timely and accurate highway inspection has become increasingly critical. Traditional manual inspection methods are not only time-consuming and labor-intensive, but they also struggle to provide consistent, high-precision detection and real-time monitoring of pavement surface defects. To overcome these limitations, we propose an Automatic Recognition of Pavement Defect (ARPD) algorithm, which leverages unmanned aerial vehicle (UAV)-based aerial imagery to automate the inspection process. The ARPD framework incorporates a backbone network based on the Selective State Space Model (S³M), which is designed to capture long-range temporal dependencies.… More >

Jun Xiong^*, Peng Yang, Bohan Chen, Zeming Chen

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069438 - 27 December 2025

Abstract The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system. However, various defects could be produced in the secondary equipment during long-term operation. The complex relationship between the defect phenomenon and multi-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods, which limits the real-time and accuracy of defect identification. Therefore, a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed. The defect data of secondary equipment is… More >

Alexander V. Savin^1,2, Elena A. Korznikova^3,4, Sergey V. Dmitriev^5,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.072786 - 09 December 2025

Abstract Due to their chiral structure, carbon nanosprings possess unique properties that are promising for nanotechnology applications. The structural transformations of carbon nanosprings in the form of spiral macromolecules derived from planar coronene and kekulene molecules (graphene helicoids and spiral nanoribbons) are analyzed using molecular dynamics simulations. The interatomic interactions are described by a force field including valence bonds, bond angles, torsional and dihedral angles, as well as van der Waals interactions. While the tension/compression of such nanosprings has been analyzed in the literature, this study investigates other modes of deformation, including bending and twisting. Depending… More >