Bo Yang^1,*, Chao Liu²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.083726 - 27 May 2026

Abstract This article has no abstract. More >

Chuanjiang Chen¹, Junyong Zhou^1,*, Binbin Du¹, Miaosi Dong^2,*, Liwen Zhang¹, Bitang Zhu³

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.081546 - 27 May 2026

Abstract Computer vision has been widely adopted in intelligent construction monitoring; however, existing studies primarily focus on identifying individual construction elements or isolated activities, with limited capability for integrated monitoring of complete construction workflows. Such workflow-level automation is a prerequisite for intelligent construction and unmanned job sites. To address the challenge of reliable visual recognition in drill-and-blast tunnel environments characterized by uneven illumination, localized glare, and dust interference, this study proposes a methodological framework for construction workflow recognition at the tunnel face using computer vision and context reasoning. The framework consists of three components: (1) a… More >

Luis Sánchez Calderón^*, David Valverde Burneo, Walter Hurtares Orrala

Structural Durability & Health Monitoring, Vol.20, No.3, 2026, DOI:10.32604/sdhm.2026.077137 - 18 May 2026

Abstract Accurate characterization of live load histories remains critical for structural safety and efficient design; however, traditional codes often overestimate in-service loads. This study introduced an AI-driven framework integrating YOLOv8 object detection and DeepFace gender classification with continuous video surveillance to monitor live loads in academic buildings. Gender classification used local anthropometric data (77 kg males, 61 kg females) for precise load estimation, with privacy ensured via local processing and anonymized metadata only. Observed peaks were substantially below Eurocode and IBC provisions, confirming code conservatism. Uncertainty propagation from detector errors (recall 0.57, ±0.02 Kn/m²) minimally impacted projections. More >

Jigar Sarda¹, Rohan Vaghela¹, Akash Kumar Bhoi², Chang-Won Yoon^3,*, Mangal Sain^4,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.080112 - 08 May 2026

Abstract Printed circuit boards (PCBs) are essential components that strongly influence the performance and reliability of modern electronic systems. However, minor and visually subtle manufacturing defects can degrade product quality and pose serious challenges for automated inspection systems. Existing deep learning–based methods often struggle to simultaneously achieve high detection accuracy, real-time processing speed, and compact model size. This study proposes an enhanced approach for real-time PCB defect detection using advanced object detection models. A dedicated dataset of bare PCBs was developed and carefully annotated with six defect categories: open circuits, missing holes, spurs, mouse bites, short… More >

Savindi Thathsarani¹, Ashan Lakshitha², Pasindu Pramodya², Praveen Perera², Rasanjali Samarakoon^1,*, Shagufta Henna³, Upaka Rathnayake^4,*

Phyton-International Journal of Experimental Botany, Vol.95, No.4, 2026, DOI:10.32604/phyton.2026.078657 - 28 April 2026

Abstract Accurately determining the optimal post-harvest storage period is still a major challenge in mango processing, especially for the Tom EJC (TEJC) variety, due to reliance on subjective visual evaluations, leading to inconsistent product quality and increased post-harvest losses. This study presents an artificial intelligence-based framework combining computer vision and physicochemical analysis to objectively predict the optimal post-harvest storage period of TEJC mango before processing. TEJC mangoes of grade one were stored for eight days at 24–28°C temperature and 66.4–80% relative humidity. Daily measurements of pH, Total Soluble Solids (TSS), firmness, and peel color parameters (L*,… More > Graphic Abstract

Zhengyu Wu¹, Kejun Kang², Yixiu Liu^3,*, Chenpu Li³

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075593 - 12 March 2026

Abstract Binary segmentation tasks in computer vision exhibit diverse appearance distributions and complex boundary characteristics. To address the limited generalization and adaptability of existing models across heterogeneous tasks, we propose Abel-Net, an Aggregated Bilateral Edge Localization Network designed as a universal framework for multi-task binary segmentation. Abel-Net integrates global and local contextual cues to enhance feature learning and edge precision. Specifically, a multi-scale feature pyramid fusion strategy is implemented via an Aggregated Skip Connection (ASC) module to strengthen feature adaptability, while the Edge Dual Localization (EDL) mechanism performs coarse-to-fine refinement through edge-aware supervision. Additionally, Edge Attention More >

Mubarak Alanazi^1,*, Junaid Rashid²

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.077956 - 26 February 2026

Abstract Wind turbine blade defect detection faces persistent challenges in separating small, low-contrast surface faults from complex backgrounds while maintaining reliability under variable illumination and viewpoints. Conventional image-processing pipelines struggle with scalability and robustness, and recent deep learning methods remain sensitive to class imbalance and acquisition variability. This paper introduces TurbineBladeDetNet, a convolutional architecture combining dual-attention mechanisms with multi-path feature extraction for detecting five distinct blade fault types. Our approach employs both channel-wise and spatial attention modules alongside an Albumentations-driven augmentation strategy to handle dataset imbalance and capture condition variability. The model achieves 97.14% accuracy, 98.65% More >

Waseem Akhtar^1,2, Mahwish Ilyas³, Romana Aziz^4,*, Ghadah Aldehim⁴, Tassawar Iqbal⁵, Muhammad Ramzan⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.071669 - 26 February 2026

Abstract Human Activity Recognition (HAR) is a novel area for computer vision. It has a great impact on healthcare, smart environments, and surveillance while is able to automatically detect human behavior. It plays a vital role in many applications, such as smart home, healthcare, human computer interaction, sports analysis, and especially, intelligent surveillance. In this paper, we propose a robust and efficient HAR system by leveraging deep learning paradigms, including pre-trained models, CNN architectures, and their average-weighted fusion. However, due to the diversity of human actions and various environmental influences, as well as a lack of… More >

Nouf Abdullah Almujally¹, Tanvir Fatima Naik Bukht², Shuaa S. Alharbi³, Asaad Algarni⁴, Ahmad Jalal^2,5, Jeongmin Park^6,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072243 - 10 February 2026

Abstract Recognising human-object interactions (HOI) is a challenging task for traditional machine learning models, including convolutional neural networks (CNNs). Existing models show limited transferability across complex datasets such as D3D-HOI and SYSU 3D HOI. The conventional architecture of CNNs restricts their ability to handle HOI scenarios with high complexity. HOI recognition requires improved feature extraction methods to overcome the current limitations in accuracy and scalability. This work proposes a Novel quantum gate-enabled hybrid CNN (QEH-CNN) for effective HOI recognition. The model enhances CNN performance by integrating quantum computing components. The framework begins with bilateral image filtering,… More >

Weishan Gao^1,2, Ye Wang^1,2, Xiaoyin Wang^1,2, Xiaochuan Jing^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073850 - 12 January 2026

Abstract In the field of intelligent surveillance, weakly supervised video anomaly detection (WSVAD) has garnered widespread attention as a key technology that identifies anomalous events using only video-level labels. Although multiple instance learning (MIL) has dominated the WSVAD for a long time, its reliance solely on video-level labels without semantic grounding hinders a fine-grained understanding of visually similar yet semantically distinct events. In addition, insufficient temporal modeling obscures causal relationships between events, making anomaly decisions reactive rather than reasoning-based. To overcome the limitations above, this paper proposes an adaptive knowledge-based guidance method that integrates external structured… More >