Dongjie Tan¹, Xiaoyu Yang², Xinxin Cao^3,*

Structural Durability & Health Monitoring, Vol.20, No.5, 2026, DOI:10.32604/sdhm.2026.081510 - 24 August 2026

Abstract Interfacial adhesion failure is the primary limiting factor in the long-term durability of epoxy-emulsified asphalt micro-surfacing pavements. However, while digital image correlation (DIC) has been extensively applied to evaluate the bulk fatigue of traditional hot-mix asphalt and concrete, its specific application to the complex bi-material interface between rigid concrete substrates and cold-mixed, thermosetting epoxy-asphalt overlays remains limited. Consequently, current research lacks real-time data on full-field strain evolution and the transitional damage localisation mechanisms during dynamic fatigue processes under extreme temperature gradients. To this goal, three-point bending fatigue tests were performed at various temperatures (ranging from… More >

Jing Cui¹, Yong Qin^2,*, Yixuan Geng³, Miao Guo^4,*, Xue Yang⁴, Wanyin Shi⁵

Structural Durability & Health Monitoring, Vol.20, No.5, 2026, DOI:10.32604/sdhm.2026.081306 - 24 August 2026

Abstract Noise barriers (NBs) play a significant role in reducing railway noise and preventing foreign-object intrusion. However, surface damage, corrosion, rust, missing components, and local deformation may gradually reduce their structural reliability and threaten railway operation safety. Because NB anomalies are diverse and defect samples are limited, it remains difficult to build a general detector using conventional supervised learning. To address this problem, this study proposes an unsupervised anomaly detection system for railway NBs using UAV imagery. First, a color-prior-based NB localization algorithm is developed in the HSV color space to extract NB regions without cumbersome More >

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 >

Bayan Alabdullah¹, Muhammad Waqas Ahmed², Mohammad Shorfuzzaman^3,*, Jasem Almotiri⁴, Mohammed Alonazi⁵, Ahmad Jalal^6,7,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.086985 - 13 August 2026

Abstract Multimodal data fusion and deep learning have opened new frontiers in the analysis of complex visual data acquired from heterogeneous sensing systems. Flood inundation mapping represents one of the most demanding applications in this domain, requiring robust interpretation of complementary but conflicting image modalities under severe real-world constraints. This paper presents CAG-Transformer, a novel multimodal AI architecture for bi-temporal flood change detection through intelligent fusion of Sentinel-1 SAR and Sentinel-2 multispectral imagery. Three tightly integrated contributions address the core challenges of heterogeneous multimodal image analysis. A Change Attention Gate (CAG) performs adaptive channel-wise representation learning,… More >

Jinshuo Ma, Yang Li^*, Can Guo, Wen Gao, Ruiming Zhang

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085999 - 13 August 2026

Abstract Underwater image restoration is severely hindered by a tightly coupled degradation process: wavelength-dependent spectral distortion combined with non-uniform, multi-scale spatial scattering. Standard Convolutional Neural Networks (CNNs) and rigid physical priors frequently fail in these dynamic environments, limited by restricted receptive fields, overlooked inter-channel spectral correlations, and severe over-enhancement in photon-starved regions. To break this bottleneck, we propose the Phased Feature Rectification Network (PFR-Net), a decoupled architecture that transforms the ill-posed restoration task into a sequential global spectral calibration and deep semantic refinement paradigm. In the first phase, an efficient Multi-Layer Perceptron (MLP)-based Color Mapping (MLP-CM)… More >

Yufei Wang¹, Jiayi Shang¹, Fang Liu^1,*, Jun Liu²

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085708 - 13 August 2026

Abstract Ship detection is an effective way of sea area supervision, which has important research value in both military and civil fields. For small ship targets in the sea scene, the deep feature map is difficult to effectively capture their subtle features, resulting in the decline of small target detection accuracy and the increase of the missing detection rate. To solve this problem, this paper proposes a detection algorithm called YOLO-MARALight, which adds a small target detection layer in the head network, uses a larger scale feature map to retain the details, and improves the discrimination… More > Graphic Abstract

Chanchan Zhao^1,#, Xiaoyu Gao^1,#, Bao Shi^2,*, Ziyang Zhang¹

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085282 - 13 August 2026

Abstract Detecting objects in unmanned aerial vehicle (UAV) imagery is challenging because most targets occupy only a small number of pixels and are often distributed in crowded regions with cluttered backgrounds. For edge-side UAV applications, the detector must also remain compact enough for real-time inference on low-power computing platforms. To meet these requirements, this study develops a YOLOv11n-based small-object detector by redesigning feature extraction, cross-scale fusion, and prediction modules. In the backbone, the proposed Dual-Context Large-Small Convolution (DCLSConv) is embedded into the C3k2 structure to form C3k2-DC, allowing the network to capture broader contextual cues while… More >

Muhammad Naeem Zafar¹, Yunfei Yin^1,*, Junaid Abbas², Bayan Alabdullah³, Khaled Alnowaiser⁴, Yunyoung Nam⁵, Zepa Yang^5,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085136 - 13 August 2026

Abstract Accurate medical image classification increasingly relies on the joint modeling of global contextual semantics and fine-grained local structural cues, since many lesions are only reliably recognized when subtle local details are interpreted within their broader anatomical context. However, most recent hybrid CNN–Transformer and global–local frameworks still extract these features in separate streams and merge them only through late-stage static fusion, without explicit bidirectional interaction during representation learning. As a result, global context cannot effectively guide the refinement of subtle local structures, and local discriminative cues cannot recalibrate higher-level semantic reasoning before classification, which limits reciprocal… More >

Kaisen Li¹, Yunwei Zhang^1,*, Guoying Sun¹, Bin Li^2,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084755 - 13 August 2026

Abstract With the development of vision-language pre-trained models, effectively exploiting high-level semantics and precisely controlling bitrate in learned image compression remains a challenging problem. Existing methods mainly rely on image feature modeling alone, making it difficult to jointly preserve fine-grained details and semantic consistency under a given bitrate budget. To address this issue, this paper proposes a learned image compression framework that integrates text-semantic guidance with content-aware bitrate control. The framework combines Bootstrapping Language-Image Pre-training (BLIP) and Contrastive Language-Image Pre-training (CLIP) to extract image semantic information, and performs conditional modulation on multi-scale visual features through feature-wise… More >

Heng Wang¹, Shichao Li¹, Long Xu^2,*, Chuqiao Wang¹, Yanzhou Feng¹, Zou Zhou^1,3,4,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083432 - 13 August 2026

Abstract The inherent challenges of small objects in remote sensing imagery encompass the degradation of fine-grained spatial details throughout the downsampling stages, semantic inconsistency during multi-level feature fusion, along with unreliable localization caused by noisy samples. To address these issues, this paper proposes an efficient small-object detector termed EMW-YOLO. An Efficient Down-sampling (EDS) module is introduced to preserve fine-grained spatial information and enhance feature representation during feature extraction through spatial rearrangement and cross-dimensional attention. A Multi-Scale Fusion and Enhancement (MSFE) architecture is further developed to improve semantic consistency across feature levels by combining local enhancement with More >