Arvind Mukundan^1,2, Riya Karmakar¹, Devansh Gupta³, Hsiang-Chen Wang^1,4,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.069646 - 10 November 2025

Abstract Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0. Manual inspection of products on assembly lines remains inefficient, prone to errors and lacks consistency, emphasizing the need for a reliable and automated inspection system. Leveraging both object detection and image segmentation approaches, this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning (DL) models. Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images… More >

Xuanhong Wang¹, Hongyu Guo¹, Jiazhen Li¹, Mingchen Wang¹, Xian Wang¹, Yijun Zhang^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-19, 2026, DOI:10.32604/cmc.2025.069482 - 10 November 2025

Abstract Over the past decade, large-scale pre-trained autoregressive and diffusion models rejuvenated the field of text-guided image generation. However, these models require enormous datasets and parameters, and their multi-step generation processes are often inefficient and difficult to control. To address these challenges, we propose CAFE-GAN, a CLIP-Projected GAN with Attention-Aware Generation and Multi-Scale Discrimination, which incorporates a pre-trained CLIP model along with several key architectural innovations. First, we embed a coordinate attention mechanism into the generator to capture long-range dependencies and enhance feature representation. Second, we introduce a trainable linear projection layer after the CLIP text… More >

Kim Sao Nguyen, Ngoc Dung Bui^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-16, 2026, DOI:10.32604/cmc.2025.069355 - 10 November 2025

Abstract Reversible data hiding (RDH) enables secret data embedding while preserving complete cover image recovery, making it crucial for applications requiring image integrity. The pixel value ordering (PVO) technique used in multi-stego images provides good image quality but often results in low embedding capability. To address these challenges, this paper proposes a high-capacity RDH scheme based on PVO that generates three stego images from a single cover image. The cover image is partitioned into non-overlapping blocks with pixels sorted in ascending order. Four secret bits are embedded into each block’s maximum pixel value, while three additional 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 >

Ghadah Naif Alwakid¹, Samabia Tehsin^2,*, Mamoona Humayun^3,*, Asad Farooq², Ibrahim Alrashdi¹, Amjad Alsirhani¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.069162 - 10 November 2025

Abstract Skin diseases affect millions worldwide. Early detection is key to preventing disfigurement, lifelong disability, or death. Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance, and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks (CNNs). We frame skin lesion recognition as graph-based reasoning and, to ensure fair evaluation and avoid data leakage, adopt a strict lesion-level partitioning strategy. Each image is first over-segmented using SLIC (Simple Linear Iterative Clustering) to produce perceptually homogeneous superpixels. These superpixels form the nodes of a region-adjacency graph whose edges encode… 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 >

Ghadah Naif Alwakid^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-25, 2026, DOI:10.32604/cmc.2025.068666 - 10 November 2025

Abstract Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder that significantly affects cognitive function, making early and accurate diagnosis essential. Traditional Deep Learning (DL)-based approaches often struggle with low-contrast MRI images, class imbalance, and suboptimal feature extraction. This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans. Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN). A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient (MCC)-based evaluation method into the design.… More >

Jianhua Yang^*, Zhiwei Lv, Changling Huo

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.068442 - 10 November 2025

Abstract Aiming at the scale adaptation of automatic driving target detection algorithms in low illumination environments and the shortcomings in target occlusion processing, this paper proposes a YOLO-LKSDS automatic driving detection model. Firstly, the Contrast-Limited Adaptive Histogram Equalisation (CLAHE) image enhancement algorithm is improved to increase the image contrast and enhance the detailed features of the target; then, on the basis of the YOLOv5 model, the Kmeans++ clustering algorithm is introduced to obtain a suitable anchor frame, and SPPELAN spatial pyramid pooling is improved to enhance the accuracy and robustness of the model for multi-scale target… More >

Hongyu Zhao^1,2, Shengsheng Wang^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-25, 2026, DOI:10.32604/cmc.2025.068426 - 10 November 2025

Abstract Driven by advancements in mobile internet technology, images have become a crucial data medium. Ensuring the security of image information during transmission has thus emerged as an urgent challenge. This study proposes a novel image encryption algorithm specifically designed for grayscale image security. This research introduces a new Cantor diagonal matrix permutation method. The proposed permutation method uses row and column index sequences to control the Cantor diagonal matrix, where the row and column index sequences are generated by a spatiotemporal chaotic system named coupled map lattice (CML). The high initial value sensitivity of the… More >

Yanting Zhang¹, Qiyue Liu^1,2, Chuanzhao Tian^1,2,*, Xuewen Li¹, Na Yang¹, Feng Zhang¹, Hongyue Zhang³

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-25, 2026, DOI:10.32604/cmc.2025.068403 - 10 November 2025

Abstract High-resolution remote sensing images (HRSIs) are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies. However, their significant scale changes and wealth of spatial details pose challenges for semantic segmentation. While convolutional neural networks (CNNs) excel at capturing local features, they are limited in modeling long-range dependencies. Conversely, transformers utilize multihead self-attention to integrate global context effectively, but this approach often incurs a high computational cost. This paper proposes a global-local multiscale context network (GLMCNet) to extract both global and local multiscale contextual information from HRSIs.… More >