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 >

Feng-Cheng Lin^*, Bo-Chiao Jan, Hui-An Wu

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079393 - 08 May 2026

Abstract With the increasing refinement of ornamental fish culture, understanding fish behavioral patterns has become critical. Fish movements not only reflect daily activity ranges but also reveal responses to environmental changes such as water currents and obstacles. However, traditional manual observation is limited by manpower and time, making it difficult to record fish behaviors over long periods stably. Existing automated tracking techniques often suffer from ID switches and track interruptions caused by rapid fish movement, occlusions, or intermingling, which in turn degrade the reliability of subsequent analyses. This paper proposes a deep learning-based multi-object fish tracking… More >

Mingfang Li, Damin Zhang^*, Qing He, Chenglong Zhou, Mingrong Li, Xiaobo Zhou

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076433 - 09 April 2026

Abstract Traffic sign detection is a critical task in autonomous driving environmental perception. However, models often suffer from degraded detection performance in complex real-world scenarios due to variable target scales, blurred fine-grained features, and complex background interference. This paper proposes an improved YOLOv8n detection model, MSD-YOLO, to address these challenges. First, a Multi-scale Detail Enhancement Module (MDEM) is designed, which achieves targeted enhancement of edge features through high-frequency residual modulation and multi-scale cooperative attention. Second, an enhanced feature pyramid network termed SG-FPN is constructed. It introduces soft nearest neighbor interpolation (SNI) for semantic-spatial aligned feature fusion… More >

Chih-Yung Huang^*, Hong-Ru Shi, Min-Yan Xie

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075398 - 12 March 2026

Abstract Quality control plays a critical role in modern manufacturing. With the rapid development of electric vehicles, 5G communications, and the semiconductor industry, high-speed and high-precision detection of surface defects on silicon carbide (SiC) wafers has become essential. This study developed an automated inspection framework for identifying surface defects on SiC wafers during the coarse grinding stage. The complex machining textures on wafer surfaces hinder conventional machine vision models, often leading to misjudgment. To address this, deep learning algorithms were applied for defect classification. Because defects are rare and imbalanced across categories, data augmentation was performed… More >

A. S. M. Masudur Rahman¹, Muhammad Zunair Zamir¹, Syed Sajid Ullah^2,*, Salman Khan², Maria Saman¹, Naqash Bahadar¹

Journal on Artificial Intelligence, Vol.8, pp. 119-136, 2026, DOI:10.32604/jai.2026.076274 - 24 February 2026

Abstract Vehicle detection plays a pivotal role in autonomous driving, traffic monitoring, and intelligent surveillance systems. While YOLOv8 offers strong real-time performance, its detection accuracy is often limited by insufficient feature stability and suboptimal multi-scale feature fusion in complex scenes. To address these issues, we propose an enhanced YOLOv8 framework that retains the original backbone and detection head for efficiency while introducing targeted improvements to the neck architecture. Specifically, the model incorporates an Exponential Moving Average (EMA) feature layer to stabilize learning through temporally smoothed feature representations, which reduces noise and enhances generalization, and integrates GhostConv… More >

Minghui Li¹, Hongbo Li^1,*, Jiaqi Zhu², Xupeng Zhang¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072406 - 12 January 2026

Abstract To address the challenge of real-time detection of unauthorized drone intrusions in complex low-altitude urban environments such as parks and airports, this paper proposes an enhanced MBS-YOLO (Multi-Branch Small Target Detection YOLO) model for anti-drone object detection, based on the YOLOv8 architecture. To overcome the limitations of existing methods in detecting small objects within complex backgrounds, we designed a C2f-Pu module with excellent feature extraction capability and a more compact parameter set, aiming to reduce the model’s computational complexity. To improve multi-scale feature fusion, we construct a Multi-Branch Feature Pyramid Network (MB-FPN) that employs a… More >

Zhonghao Wang^1,2, Xin Liu^1,2,*, Changhua Yue³, Haiwen Yuan⁴

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.071813 - 09 December 2025

Abstract To address critical challenges in nighttime ship detection—high small-target missed detection (over 20%), insufficient lightweighting, and limited generalization due to scarce, low-quality datasets—this study proposes a systematic solution. First, a high-quality Night-Ships dataset is constructed via CycleGAN-based day-night transfer, combined with a dual-threshold cleaning strategy (Laplacian variance sharpness filtering and brightness-color deviation screening). Second, a Cross-stage Lightweight Fusion-You Only Look Once version 8 (CLF-YOLOv8) is proposed with key improvements: the Neck network is reconstructed by replacing Cross Stage Partial (CSP) structure with the Cross Stage Partial Multi-Scale Convolutional Block (CSP-MSCB) and integrating Bidirectional Feature Pyramid More >

Yuqiang Wu^1,2, Zhao Ji¹, Guanqi You¹, Zihan Zhang¹, Chaoping Lu³, Huanliang Xu¹, Zhaoyu Zhai^1,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-22, 2026, DOI:10.32604/cmc.2025.070414 - 09 December 2025

Abstract Understanding fish movement trajectories in aquaculture is essential for practical applications, such as disease warning, feeding optimization, and breeding management. These trajectories reveal key information about the fish’s behavior, health, and environmental adaptability. However, when multi-object tracking (MOT) algorithms are applied to the high-density aquaculture environment, occlusion and overlapping among fish may result in missed detections, false detections, and identity switching problems, which limit the tracking accuracy. To address these issues, this paper proposes FishTracker, a MOT algorithm, by utilizing a Tracking-by-Detection framework. First, the neck part of the YOLOv8 model is enhanced by introducing… More >

Zia Ur Rehman¹, Ahmad Syed^2,*, Abu Tayab³, Ghanshyam G. Tejani^4,5,*, Doaa Sami Khafaga⁶, El-Sayed M. El-kenawy^7,8

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-25, 2026, DOI:10.32604/cmc.2025.070310 - 09 December 2025

Abstract Object detection, a major challenge in computer vision and pattern recognition, plays a significant part in many applications, crossing artificial intelligence, face recognition, and autonomous driving. It involves focusing on identifying the detection, localization, and categorization of targets in images. A particularly important emerging task is distinguishing real animals from toy replicas in real-time, mostly for smart camera systems in both urban and natural environments. However, that difficult task is affected by factors such as showing angle, occlusion, light intensity, variations, and texture differences. To tackle these challenges, this paper recommends Group Sparse YOLOv8 (You… More >

Qing Guo^1,2, Juwei Zhang^1,2,3,*, Bingyi Ren^1,2

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.069053 - 10 November 2025

Abstract Traffic sign detection is an important part of autonomous driving, and its recognition accuracy and speed are directly related to road traffic safety. Although convolutional neural networks (CNNs) have made certain breakthroughs in this field, in the face of complex scenes, such as image blur and target occlusion, the traffic sign detection continues to exhibit limited accuracy, accompanied by false positives and missed detections. To address the above problems, a traffic sign detection algorithm, You Only Look Once-based Skip Dynamic Way (YOLO-SDW) based on You Only Look Once version 8 small (YOLOv8s), is proposed. Firstly,… More >