Meng Wang¹, Jinghan Cai¹, Wenzheng Liu¹, Xue Yang¹, Jingjing Zhang¹, Qiangmin Zhou¹, Fanzhen Wang¹, Hang Zhang^1,*, Tonghai Liu^2,*

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2025.075541 - 30 January 2026

Abstract Tomato is a major economic crop worldwide, and diseases on tomato leaves can significantly reduce both yield and quality. Traditional manual inspection is inefficient and highly subjective, making it difficult to meet the requirements of early disease identification in complex natural environments. To address this issue, this study proposes an improved YOLO11-based model, YOLO-SPDNet (Scale Sequence Fusion, Position-Channel Attention, and Dual Enhancement Network). The model integrates the SEAM (Self-Ensembling Attention Mechanism) semantic enhancement module, the MLCA (Mixed Local Channel Attention) lightweight attention mechanism, and the SPA (Scale-Position-Detail Awareness) module composed of SSFF (Scale Sequence Feature… More >

Xiang Luo¹, Yuxuan Peng², Renghong Xie¹, Peng Li³, Yuwen Qian^3,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073700 - 12 January 2026

Abstract Deep learning has made significant progress in the field of oriented object detection for remote sensing images. However, existing methods still face challenges when dealing with difficult tasks such as multi-scale targets, complex backgrounds, and small objects in remote sensing. Maintaining model lightweight to address resource constraints in remote sensing scenarios while improving task completion for remote sensing tasks remains a research hotspot. Therefore, we propose an enhanced multi-scale feature extraction lightweight network EM-YOLO based on the YOLOv8s architecture, specifically optimized for the characteristics of large target scale variations, diverse orientations, and numerous small objects… More >

Wang Zhang^1,#, Haozhuo Cao^2,#, Qiangqiang Yao^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072948 - 12 January 2026

Abstract Recent advances in deep learning have significantly improved image deblurring; however, existing approaches still suffer from limited global context modeling, inadequate detail restoration, and poor texture or edge perception, especially under complex dynamic blur. To address these challenges, we propose the Multi-Resolution Fusion Network (MRFNet), a blind multi-scale deblurring framework that integrates progressive residual connectivity for hierarchical feature fusion. The network employs a three-stage design: (1) TransformerBlocks capture long-range dependencies and reconstruct coarse global structures; (2) Nonlinear Activation Free Blocks (NAFBlocks) enhance local detail representation and mid-level feature fusion; and (3) an optimized residual subnetwork… 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 >

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 >

Huan Zeng, Jianxun Zhang^*, Hongji Chen, Xinwei Zhu

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3879-3896, 2025, DOI:10.32604/cmc.2025.066803 - 23 September 2025

Abstract Semantic segmentation in street scenes is a crucial technology for autonomous driving to analyze the surrounding environment. In street scenes, issues such as high image resolution caused by a large viewpoints and differences in object scales lead to a decline in real-time performance and difficulties in multi-scale feature extraction. To address this, we propose a bilateral-branch real-time semantic segmentation method based on semantic information distillation (BSDNet) for street scene images. The BSDNet consists of a Feature Conversion Convolutional Block (FCB), a Semantic Information Distillation Module (SIDM), and a Deep Aggregation Atrous Convolution Pyramid Pooling (DASP). More >

Majid Harouni^1,*, Vishakha Goyal¹, Gabrielle Feldman¹, Sam Michael², Ty C. Voss¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 331-366, 2025, DOI:10.32604/cmc.2025.067915 - 29 August 2025

Abstract Semantic segmentation plays a foundational role in biomedical image analysis, providing precise information about cellular, tissue, and organ structures in both biological and medical imaging modalities. Traditional approaches often fail in the face of challenges such as low contrast, morphological variability, and densely packed structures. Recent advancements in deep learning have transformed segmentation capabilities through the integration of fine-scale detail preservation, coarse-scale contextual modeling, and multi-scale feature fusion. This work provides a comprehensive analysis of state-of-the-art deep learning models, including U-Net variants, attention-based frameworks, and Transformer-integrated networks, highlighting innovations that improve accuracy, generalizability, and computational More >

Shuo Zhang, Hanwu He, Yueming Wu^*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 801-826, 2025, DOI:10.32604/cmc.2025.066540 - 29 August 2025

Abstract Virtual maintenance, as an important means of industrial training and education, places strict requirements on the accuracy of participant pose perception and assessment of motion standardization. However, existing research mainly focuses on human pose estimation in general scenarios, lacking specialized solutions for maintenance scenarios. This paper proposes a virtual maintenance human pose estimation method based on multi-scale feature enhancement (VMHPE), which integrates adaptive input feature enhancement, multi-scale feature correction for improved expression of fine movements and complex poses, and multi-scale feature fusion to enhance keypoint localization accuracy. Meanwhile, this study constructs the first virtual maintenance-specific… More >

Chuanchuan Wang^1,2, Ahmad Sufril Azlan Mohamed^2,*, Xiao Yang ², Hao Zhang ², Xiang Li¹, Mohd Halim Bin Mohd Noor ²

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 855-874, 2025, DOI:10.32604/cmc.2025.066343 - 29 August 2025

Abstract Classroom behavior recognition is a hot research topic, which plays a vital role in assessing and improving the quality of classroom teaching. However, existing classroom behavior recognition methods have challenges for high recognition accuracy with datasets with problems such as scenes with blurred pictures, and inconsistent objects. To address this challenge, we proposed an effective, lightweight object detector method called the RFNet model (YOLO-FR). The YOLO-FR is a lightweight and effective model. Specifically, for efficient multi-scale feature extraction, effective feature pyramid shared convolutional (FPSC) was designed to improve the feature extract performance by leveraging convolutional… More >

Darthy Rabecka V^1,*, Britto Pari J¹, Man-Fai Leung^2,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 827-853, 2025, DOI:10.32604/cmc.2025.064924 - 29 August 2025

Abstract Techniques in deep learning have significantly boosted the accuracy and productivity of computer vision segmentation tasks. This article offers an intriguing architecture for semantic, instance, and panoptic segmentation using EfficientNet-B7 and Bidirectional Feature Pyramid Networks (Bi-FPN). When implemented in place of the EfficientNet-B5 backbone, EfficientNet-B7 strengthens the model’s feature extraction capabilities and is far more appropriate for real-world applications. By ensuring superior multi-scale feature fusion, Bi-FPN integration enhances the segmentation of complex objects across various urban environments. The design suggested is examined on rigorous datasets, encompassing Cityscapes, Common Objects in Context, KITTI Karlsruhe Institute of… More >