Xue Yang¹, Shunpeng Yang¹, Wanying Shi^2,*, Weizhong Yuan¹, Sihui Long¹, Ruixiao Sun³, Cheng Yang⁴

Structural Durability & Health Monitoring, Vol.20, No.5, 2026, DOI:10.32604/sdhm.2026.078468 - 24 August 2026

Abstract Unmanned aerial vehicle (UAV) imaging techniques have emerged as a promising solution to boost the accuracy and dependability of visual monitoring for railway facilities and peripheral ecological environments, garnering widespread research interest in recent years. Nevertheless, aerial images acquired by UAVs are prone to severe quality deterioration in fog and haze weather scenarios, which greatly hinders the progress and effectiveness of railway routine inspection work. As modern railway systems pursue higher operational safety benchmarks and intelligent rail transit technologies achieve iterative breakthroughs, video monitoring systems have evolved into indispensable core equipment for identifying and early… More >

Khanh Nguyen-Trong^1,*, Tan Nguyen-Thi-Thanh²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083365 - 23 July 2026

Abstract Automated wood surface defect detection is difficult to evaluate reliably because defects are often small, low-contrast, and visually confounded by natural wood texture, while reported performance can vary substantially with benchmark design and domain shift. To address this issue, we conduct a comparative study across three practically relevant settings: a curated seven-class benchmark, a broader in-domain seven-class protocol derived from the same source dataset, and supervised adaptation to a low-resource Vietnamese target domain. We compare lightweight two-stage detectors based on Faster Region-based Convolutional Neural Network (Faster R-CNN) with MobileNetV3-FPN against a compact You Only Look… More >

Jianfeng Liu¹, Yongjiao Yang¹, Kangyi Yang¹, Changhua Hu¹, Zijia Xu¹, Qingguo Shi², Yi Su^2,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082804 - 15 June 2026

Abstract Power enterprise inspection and supervision require greater intelligence, efficiency, and standardization; however, existing approaches are limited by inefficient knowledge retrieval, inaccurate issue identification, and insufficient support for standardized reporting and rectification tracking. This study proposes a lightweight, domain-adaptive large language model (LLM) framework based on Low-Rank Adaptation (LoRA), integrating Retrieval-Augmented Generation (RAG) and structured prompt engineering to enable evidence-grounded inspection tasks. The framework achieves parameter-efficient adaptation through low-rank decomposition and constructs a domain-specific multimodal knowledge base, enhancing output traceability, consistency, and task generalization. A key contribution is the introduction of a Sensitive Information Control Gate, More >

Haoyu Li¹, Jiayi Wang¹, Zhaoyu Wu¹, Shuo Yan¹, Ziqi Zhang¹, Yang Gao^2,3, Genwang Peng^2,3, Zhiwei Cao^2,*

Structural Durability & Health Monitoring, Vol.20, No.3, 2026, DOI:10.32604/sdhm.2026.077736 - 18 May 2026

Abstract Key components of rail transit systems, such as tracks and vehicle bodies, are prone to developing various types and manifestations of defects during long-term operation. These defects not only accelerate component aging and failure but also pose serious threats to train operational safety. Among existing intelligent detection methods, they mostly rely solely on visible light images demonstrate limited robustness in complex scenarios. This limitation stems from their high dependence on ambient lighting conditions, rendering them insufficient to meet practical railway inspection requirements. While mainstream multimodal detection methods incorporate the complementary strengths of heterogeneous data sources,… More >

Jigar Sarda¹, Rohan Vaghela¹, Akash Kumar Bhoi², Chang-Won Yoon^3,*, Mangal Sain^4,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.080112 - 08 May 2026

Abstract Printed circuit boards (PCBs) are essential components that strongly influence the performance and reliability of modern electronic systems. However, minor and visually subtle manufacturing defects can degrade product quality and pose serious challenges for automated inspection systems. Existing deep learning–based methods often struggle to simultaneously achieve high detection accuracy, real-time processing speed, and compact model size. This study proposes an enhanced approach for real-time PCB defect detection using advanced object detection models. A dedicated dataset of bare PCBs was developed and carefully annotated with six defect categories: open circuits, missing holes, spurs, mouse bites, short… More >

Xianghui Li^1,2, Xin Fu³, Libo Pan², Fancong Zeng^1,2,*, Zhijiang Zuo^1,2

Structural Durability & Health Monitoring, Vol.20, No.2, 2026, DOI:10.32604/sdhm.2025.073124 - 31 March 2026

Abstract The present study proposes an autonomous visual inspection system based on Wall-Climbing Robot (WCR), with a view to addressing the shortcomings of traditional building crack detection methods, namely their low measurement accuracy, high manual dependence and insufficient environmental adaptability. The system has been developed to construct a crack recognition model with robust illumination adaptation by fusing the improved YOLOv5s target detection algorithm with the Canny edge enhancement algorithm. The system has been realized as a lightweight deployment on an embedded device (MaixCAM). The robot platform employs a design scheme integrating a dual-chamber negative pressure adsorption… More >

Trinh Thi Nhu Quynh¹, Hoa-Cuc. Nguyen², Bich-Ngoc. Mach², Thanh Q. Nguyen^3,4,*

Structural Durability & Health Monitoring, Vol.20, No.2, 2026, DOI:10.32604/sdhm.2025.070202 - 31 March 2026

Abstract This study introduces a method for processing various types of random real-world signals from bridges in both experimental models and real-world scenarios using a wireless sensor system. By analyzing and processing signals collected during actual traffic on bridges, the study identifies and provides parameters that meet current quality inspection requirements to ensure the safety of bridge users. The parameters investigated in this study include deformation, natural frequency, amplitude, impact factor, and damping coefficient. Research has determined and highlighted key parameters to assess the quality of bridge spans to meet quality inspection standards. Using actual traffic More >

Yanping Chen¹, Zhengxin Zhan¹, Xiaohui Yan¹, Le Zou^1,*, Yucheng Zhong¹, Hailei Wang²

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075459 - 12 March 2026

Abstract With the increasing complexity of substation inspection tasks, achieving efficient and safe path planning for Unmanned Aerial Vehicles in densely populated and structurally complex three-dimensional (3D) environments remains a critical challenge. To address this problem, this paper proposes an improved path planning algorithm—Random Geometric Graph (RGG)-guided Rapidly-exploring Random Tree (R-RRT)—based on the classical Rapidly-exploring Random Tree (RRT) framework. First, a refined 3D occupancy grid map is constructed from Light Detection and Ranging point cloud data through ground filtering, noise removal, coordinate transformation, and obstacle inflation using spherical structuring elements. During the planning stage, a dynamic… More >

Jingjing Liu¹, Chuanyang Liu^1,2,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075348 - 12 March 2026

Abstract Intelligent inspection of transmission lines enables efficient automated fault detection by integrating artificial intelligence, robotics, and other related technologies. It plays a key role in ensuring power grid safety, reducing operation and maintenance costs, driving the digital transformation of the power industry, and facilitating the achievement of the dual-carbon goals. This review focuses on vision-based power line inspection, with deep learning as the core perspective to systematically analyze the latest research advancements in this field. Firstly, at the technical foundation level, it elaborates on deep learning algorithms for intelligent transmission line inspection based on image… 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 >