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 >

Asaad Algarni¹, Aysha Naseer ², Mohammed Alshehri³, Yahya AlQahtani⁴, Abdulmonem Alshahrani⁴, Jeongmin Park^5,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1981-1998, 2025, DOI:10.32604/cmc.2025.064268 - 29 August 2025

Abstract Remote sensing plays a pivotal role in environmental monitoring, disaster relief, and urban planning, where accurate scene classification of aerial images is essential. However, conventional convolutional neural networks (CNNs) struggle with long-range dependencies and preserving high-resolution features, limiting their effectiveness in complex aerial image analysis. To address these challenges, we propose a Hybrid HRNet-Swin Transformer model that synergizes the strengths of HRNet-W48 for high-resolution segmentation and the Swin Transformer for global feature extraction. This hybrid architecture ensures robust multi-scale feature fusion, capturing fine-grained details and broader contextual relationships in aerial imagery. Our methodology begins with… More >

Yazeed Alkhrijah^1,2, Shehzad Khalid³, Syed Muhammad Usman^4,*, Amina Jameel³, Danish Hamid⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1113-1141, 2025, DOI:10.32604/cmes.2025.068726 - 31 July 2025

Abstract Arabic Sign Language (ArSL) recognition plays a vital role in enhancing the communication for the Deaf and Hard of Hearing (DHH) community. Researchers have proposed multiple methods for automated recognition of ArSL; however, these methods face multiple challenges that include high gesture variability, occlusions, limited signer diversity, and the scarcity of large annotated datasets. Existing methods, often relying solely on either skeletal data or video-based features, struggle with generalization and robustness, especially in dynamic and real-world conditions. This paper proposes a novel multimodal ensemble classification framework that integrates geometric features derived from 3D skeletal joint… More >

Zhengji Li¹, Fazhan Xiong¹, Boyun Huang¹, Meihui Li¹, Xi Xiao², Yingrui Ji^3,4, Jiacheng Xie^1,2, Aokun Liang⁵, Hao Xu^6,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5613-5635, 2025, DOI:10.32604/cmc.2025.066188 - 30 July 2025

Abstract Accurate and real-time road defect detection is essential for ensuring traffic safety and infrastructure maintenance. However, existing vision-based methods often struggle with small, sparse, and low-resolution defects under complex road conditions. To address these limitations, we propose Multi-Scale Guided Detection YOLO (MGD-YOLO), a novel lightweight and high-performance object detector built upon You Only Look Once Version 5 (YOLOv5). The proposed model integrates three key components: (1) a Multi-Scale Dilated Attention (MSDA) module to enhance semantic feature extraction across varying receptive fields; (2) Depthwise Separable Convolution (DSC) to reduce computational cost and improve model generalization; and More >

Haiyan Chen^*, Rong Li

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5693-5712, 2025, DOI:10.32604/cmc.2025.065339 - 30 July 2025

Abstract The detection of Ochotona Curzoniae serves as a fundamental component for estimating the population size of this species and for analyzing the dynamics of its population fluctuations. In natural environments, the pixels representing Ochotona Curzoniae constitute a small fraction of the total pixels, and their distinguishing features are often subtle, complicating the target detection process. To effectively extract the characteristics of these small targets, a feature fusion approach that utilizes up-sampling and channel integration from various layers within a CNN can significantly enhance the representation of target features, ultimately improving detection accuracy. However, the top-down… More >

Siqi Zhang, Shengda Pan^*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4655-4676, 2025, DOI:10.32604/cmc.2025.064864 - 30 July 2025

Abstract Infrared images typically exhibit diverse backgrounds, each potentially containing noise and target-like interference elements. In complex backgrounds, infrared small targets are prone to be submerged by background noise due to their low pixel proportion and limited available features, leading to detection failure. To address this problem, this paper proposes an Attention Shift-Invariant Cross-Evolutionary Feature Fusion Network (ASCFNet) tailored for the detection of infrared weak and small targets. The network architecture first designs a Multidimensional Lightweight Pixel-level Attention Module (MLPA), which alleviates the issue of small-target feature suppression during deep network propagation by combining channel reshaping,… More >

Ronghua Wang¹, Shibao Sun^1,*, Pengcheng Zhao^1,*, Xianglan Yang², Xingjia Wei¹, Changyang Hu¹

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3519-3539, 2025, DOI:10.32604/cmc.2025.066212 - 03 July 2025

Abstract The capacity to diagnose faults in rolling bearings is of significant practical importance to ensure the normal operation of the equipment. Frequency-domain features can effectively enhance the identification of fault modes. However, existing methods often suffer from insufficient frequency-domain representation in practical applications, which greatly affects diagnostic performance. Therefore, this paper proposes a rolling bearing fault diagnosis method based on a Multi-Scale Fusion Network (MSFN) using the Time-Division Fourier Transform (TDFT). The method constructs multi-scale channels to extract time-domain and frequency-domain features of the signal in parallel. A multi-level, multi-scale filter-based approach is designed to More >

Yuchao Xia, Jing Qiu^*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2531-2547, 2025, DOI:10.32604/cmc.2025.064635 - 03 July 2025

Abstract In recent years, fungal diseases affecting grape crops have attracted significant attention. Currently, the assessment of black rot severity mainly depends on the ratio of lesion area to leaf surface area. However, effectively and accurately segmenting leaf lesions presents considerable challenges. Existing grape leaf lesion segmentation models have several limitations, such as a large number of parameters, long training durations, and limited precision in extracting small lesions and boundary details. To address these issues, we propose an enhanced DeepLabv3+ model incorporating Strip Pooling, Content-Guided Fusion, and Convolutional Block Attention Module (SFC_DeepLabv3+), an enhanced lesion segmentation method based… More >

Qiang Ma^1,2,3,4,5, Zhuopei Wei^1,2, Kai Yang^1,2,*, Long Tian^1,2, Zepeng Li^1,2

Structural Durability & Health Monitoring, Vol.19, No.4, pp. 1011-1035, 2025, DOI:10.32604/sdhm.2025.060596 - 30 June 2025

Abstract An intelligent diagnosis method based on self-adaptive Wasserstein dual generative adversarial networks and feature fusion is proposed due to problems such as insufficient sample size and incomplete fault feature extraction, which are commonly faced by rolling bearings and lead to low diagnostic accuracy. Initially, dual models of the Wasserstein deep convolutional generative adversarial network incorporating gradient penalty (1D-2DWDCGAN) are constructed to augment the original dataset. A self-adaptive loss threshold control training strategy is introduced, and establishing a self-adaptive balancing mechanism for stable model training. Subsequently, a diagnostic model based on multidimensional feature fusion is designed,… More >

Zhe Chen^*, Yinyang Zhang, Sihao Xing

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1787-1803, 2025, DOI:10.32604/cmc.2025.065238 - 09 June 2025

Abstract Unmanned aerial vehicle (UAV) imagery poses significant challenges for object detection due to extreme scale variations, high-density small targets (68% in VisDrone dataset), and complex backgrounds. While YOLO-series models achieve speed-accuracy trade-offs via fixed convolution kernels and manual feature fusion, their rigid architectures struggle with multi-scale adaptability, as exemplified by YOLOv8n’s 36.4% mAP and 13.9% small-object AP on VisDrone2019. This paper presents YOLO-LE, a lightweight framework addressing these limitations through three novel designs: (1) We introduce the C2f-Dy and LDown modules to enhance the backbone’s sensitivity to small-object features while reducing backbone parameters, thereby improving More >