Jiang Mi¹, Zhijian Gan¹, Pengliu Tan^2,*, Xin Chang², Zhi Wang², Haisheng Xie²

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-22, 2026, DOI:10.32604/cmc.2025.069348 - 10 November 2025

Abstract In response to the challenges in highway pavement distress detection, such as multiple defect categories, difficulties in feature extraction for different damage types, and slow identification speeds, this paper proposes an enhanced pavement crack detection model named Star-YOLO11. This improved algorithm modifies the YOLO11 architecture by substituting the original C3k2 backbone network with a Star-s50 feature extraction network. The enhanced structure adjusts the number of stacked layers in the StarBlock module to optimize detection accuracy and improve model efficiency. To enhance the accuracy of pavement crack detection and improve model efficiency, three key modifications to… More >

Zhongliang Wei^1,*, Jianlong An¹, Chang Su²

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.069335 - 10 November 2025

Abstract Images taken in dim environments frequently exhibit issues like insufficient brightness, noise, color shifts, and loss of detail. These problems pose significant challenges to dark image enhancement tasks. Current approaches, while effective in global illumination modeling, often struggle to simultaneously suppress noise and preserve structural details, especially under heterogeneous lighting. Furthermore, misalignment between luminance and color channels introduces additional challenges to accurate enhancement. In response to the aforementioned difficulties, we introduce a single-stage framework, M2ATNet, using the multi-scale multi-attention and Transformer architecture. First, to address the problems of texture blurring and residual noise, we design… More >

Zhiyong Deng¹, Yanchen Ye², Jiangling Guo^1,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.069090 - 10 November 2025

Abstract With the rapid expansion of drone applications, accurate detection of objects in aerial imagery has become crucial for intelligent transportation, urban management, and emergency rescue missions. However, existing methods face numerous challenges in practical deployment, including scale variation handling, feature degradation, and complex backgrounds. To address these issues, we propose Edge-enhanced and Detail-Capturing You Only Look Once (EHDC-YOLO), a novel framework for object detection in Unmanned Aerial Vehicle (UAV) imagery. Based on the You Only Look Once version 11 nano (YOLOv11n) baseline, EHDC-YOLO systematically introduces several architectural enhancements: (1) a Multi-Scale Edge Enhancement (MSEE) module… More >

Yingyong Zou^*, Yu Zhang, Long Li, Tao Liu, Xingkui Zhang

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-24, 2026, DOI:10.32604/cmc.2025.068246 - 10 November 2025

Abstract Fault diagnosis of rolling bearings is crucial for ensuring the stable operation of mechanical equipment and production safety in industrial environments. However, due to the nonlinearity and non-stationarity of collected vibration signals, single-modal methods struggle to capture fault features fully. This paper proposes a rolling bearing fault diagnosis method based on multi-modal information fusion. The method first employs the Hippopotamus Optimization Algorithm (HO) to optimize the number of modes in Variational Mode Decomposition (VMD) to achieve optimal modal decomposition performance. It combines Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) to extract temporal features… More >

Sidra Naseem¹, Rashid Jahangir^1,*, Nazik Alturki², Faheem Shehzad³, Muhammad Sami Ullah⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2481-2501, 2025, DOI:10.32604/cmes.2025.072575 - 26 November 2025

Abstract Diagnosing gastrointestinal tract diseases is a critical task requiring accurate and efficient methodologies. While deep learning models have significantly advanced medical image analysis, challenges such as imbalanced datasets and redundant features persist. This study proposes a novel framework that customizes two deep learning models, NasNetMobile and ResNet50, by incorporating bottleneck architectures, named as NasNeck and ResNeck, to enhance feature extraction. The feature vectors are fused into a combined vector, which is further optimized using an improved Whale Optimization Algorithm to minimize redundancy and improve discriminative power. The optimized feature vector is then classified using artificial… More >

Diyar Wirya Omar Ameenulhakeem^*, Osman Nuri Uçan

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5671-5702, 2025, DOI:10.32604/cmc.2025.070422 - 23 October 2025

Abstract Face antispoofing has received a lot of attention because it plays a role in strengthening the security of face recognition systems. Face recognition is commonly used for authentication in surveillance applications. However, attackers try to compromise these systems by using spoofing techniques such as using photos or videos of users to gain access to services or information. Many existing methods for face spoofing face difficulties when dealing with new scenarios, especially when there are variations in background, lighting, and other environmental factors. Recent advancements in deep learning with multi-modality methods have shown their effectiveness in… More >

Ming Chen^1,*, Yanfei Niu², Ping Qi¹, Fucheng Wang¹

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5015-5035, 2025, DOI:10.32604/cmc.2025.067951 - 23 October 2025

Abstract This research addresses the critical challenge of enhancing satellite images captured under low-light conditions, which suffer from severely degraded quality, including a lack of detail, poor contrast, and low usability. Overcoming this limitation is essential for maximizing the value of satellite imagery in downstream computer vision tasks (e.g., spacecraft on-orbit connection, spacecraft surface repair, space debris capture) that rely on clear visual information. Our key novelty lies in an unsupervised generative adversarial network featuring two main contributions: (1) an improved U-Net (IU-Net) generator with multi-scale feature fusion in the contracting path for richer semantic feature… More >

Huayu Li^1,2, Xinxin Chen^1,2, Lizhuang Tan^3,4,*, Konstantin I. Kostromitin^5,6, Athanasios V. Vasilakos⁷, Peiying Zhang^1,2

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 4133-4153, 2025, DOI:10.32604/cmc.2025.069690 - 23 September 2025

Abstract To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising from modal heterogeneity during fusion, while also capturing shared information across modalities, this paper proposes a Multi-modal Pre-synergistic Entity Alignment model based on Cross-modal Mutual Information Strategy Optimization (MPSEA). The model first employs independent encoders to process multi-modal features, including text, images, and numerical values. Next, a multi-modal pre-synergistic fusion mechanism integrates graph structural and visual modal features into the textual modality as preparatory information. This pre-fusion strategy enables unified perception of heterogeneous modalities at the More >

Gang Li^1,#, Ru Wang^1,#, Yang Zhang^2,*, Chuanyun Xu², Xinyu Fan¹, Zheng Zhou¹, Pengfei Lv¹, Zihan Ruan¹

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3267-3288, 2025, DOI:10.32604/cmc.2025.067763 - 23 September 2025

Abstract Currently, challenges such as small object size and occlusion lead to a lack of accuracy and robustness in small object detection. Since small objects occupy only a few pixels in an image, the extracted features are limited, and mainstream downsampling convolution operations further exacerbate feature loss. Additionally, due to the occlusion-prone nature of small objects and their higher sensitivity to localization deviations, conventional Intersection over Union (IoU) loss functions struggle to achieve stable convergence. To address these limitations, LR-Net is proposed for small object detection. Specifically, the proposed Lossless Feature Fusion (LFF) method transfers spatial… More >

Zihan Jia, Bowen Ma, Dongyue Chen^*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2873-2891, 2025, DOI:10.32604/cmc.2025.067460 - 23 September 2025

Abstract In real-world scenarios, dual-view X-ray machines have outnumbered single-view X-ray machines due to their ability to provide comprehensive internal information about the baggage, which is important for identifying prohibited items that are not visible in one view due to rotation or overlap. However, existing work still focuses mainly on single-view, and the limited dual-view based work only performs simple information fusion at the feature or decision level and lacks effective utilization of the complementary information hidden in dual view. To this end, this paper proposes an end-to-end dual-view prohibited item detection method, the core of… More >