Sang-Hyun Lee^*, Qingtao Meng

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077207 - 09 April 2026

Abstract Accurate and timely detection of rice leaf diseases is critical for ensuring global food security and maximizing agricultural yields. However, existing deep learning methods often struggle to balance the high accuracy required for detecting multi-scale lesions in complex field environments with the computational efficiency necessary for edge device deployment. This paper proposes You Only Look Once for Lightweight Detection (YOLOv11-LD), a lightweight object detection model for multi-scale rice leaf disease detection in real paddy field environments. The model is built on YOLOv11n and integrates a Re-parameterized Vision Transformer (RepViT) backbone, a Bidirectional Feature Pyramid Network… More >

Sudhir Kumar Pandey¹, Jian-Xun Mi^1,*, Zahid Ullah², Mona Jamjoom³

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.074811 - 09 April 2026

Abstract Adversarial examples in object detection frequently fail to transfer between different models because attacks overfit to the source model’s architecture and feature space. We propose AugTrans, a framework that addresses this limitation through input-space regularization. Our key innovation is a multi-stage augmentation pipeline that incorporates object-level semantic awareness into transformation design. The pipeline comprises three novel components: dynamic object-centric rotation with adaptive scheduling, multi-box aware resizing based on ground-truth annotations, and composite noise injection. These transformations are integrated within the Expectation over Transformation (EOT) framework. By optimizing perturbations to remain effective across semantically meaningful transformations, our… More >

Yaguan Wang¹, Linlin Kou², Yang Gao^3,*, Qiang Sun¹, Yong Qin³, Genwang Peng³

Structural Durability & Health Monitoring, Vol.20, No.2, 2026, DOI:10.32604/sdhm.2025.072538 - 31 March 2026

Abstract The fasteners employed in the railway tracks are susceptible to defects arising from their intricate composition. Foreign objects are frequently observed on the track bed in an open environment. These two types of defects pose potential threats to high-speed trains, thus necessitating timely and accurate track inspection. The majority of extant automatic inspection methods are predicated on the utilization of single visible light data, and the efficacy of the algorithmic processes is influenced by complex environments. Furthermore, due to the single information dimension, the detection accuracy of defects in similar, occluded, and small object categories… More >

Yajuan Lu^*, Yongtao Hu, Jie Li, Jinping Zhang, Jingjing Si

Structural Durability & Health Monitoring, Vol.20, No.2, 2026, DOI:10.32604/sdhm.2025.071110 - 31 March 2026

Abstract To address challenges in wind turbine blade defect detection models, primarily due to insufficient feature extraction capabilities and the difficulty of deploying models on drone-type edge devices, this study proposes a wind turbine blade defect detection model, WtCS-YOLO11, that incorporates multiscale feature extraction and an attention mechanism. Firstly, the cross-stage partial with two kernels and a wavelet convolution module (C3k2_WTConv) is proposed by introducing wavelet convolution into the module. The cross-stage partial with two kernels (C3k2) module in the necking network is replaced with the C3k2_WTConv module to increase the model’s receptive field, enable multiscale… More >

Thi-Loan Nguyen^1,2,*, Huy-Nam Chu³, The-Thanh Hua³, Trung-Nghia Phung², Van-Hung Le^3,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076732 - 12 March 2026

Abstract To support the process of grasping objects on a tabletop for the blind or robotic arm, it is necessary to address fundamental computer vision tasks, such as detecting, recognizing, and locating objects in space, and determining the position of the grasping information. These results can then be used to guide the visually impaired or to execute grasping tasks with a robotic arm. In this paper, we collected, annotated, and published the benchmark TQU-GraspingObject dataset for testing, validation, and evaluation of deep learning (DL) models for detecting, recognizing, and localizing grasping objects in 2D and 3D… More >

Xinyue Zhang¹, Yuxuan Zhao², Jeremy S. Smith³, Yuechun Wang⁴, Gabriela Mogos⁵, Ka Lok Man¹, Yutao Yue^6,7,8,9, Young-Ae Jung^10,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075415 - 12 March 2026

Abstract Intelligent Transportation Systems (ITS) represent a cornerstone in modern traffic management, leveraging surveillance cameras as primary visual sensors to monitor road conditions. However, the fixed characteristics of public surveillance cameras, coupled with inherent image resolution limitations, pose significant challenges for Small Object Detection (SOD) in traffic surveillance. To address these challenges, this paper proposes Ghost-Attention YOLO (GA-YOLO), a lightweight model derived from YOLOv8 and specifically designed for traffic SOD. To enhance the attention of small targets and critical features, a novel channel-spatial attention mechanism, termed Small-object Extend Attention (SEA), is introduced. In addition, the original… More >

Zhichao Yu¹, Jiahui Yu¹, Simon James Fong^1,*, Yaoyang Wu^1,2

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074899 - 12 March 2026

Abstract Accurately recognizing driver distraction is critical for preventing traffic accidents, yet current detection models face two persistent challenges. First, distractions are often fine-grained, involving subtle cues such as brief eye closures or partial yawns, which are easily missed by conventional detectors. Second, in real-world scenarios, drivers frequently exhibit overlapping behaviors, such as simultaneously holding a cup, closing their eyes, and yawning, leading to multiple detection boxes and degraded model performance. Existing approaches fail to robustly address these complexities, resulting in limited reliability in safety critical applications. To overcome these pain points, we propose YOLO-Drive, a… More >

Wang Cheng¹, Zhuodong Liu², Xiangyu Li^3,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074250 - 12 March 2026

Abstract Smart pest control is crucial for building farm resilience and ensuring sustainable agriculture in the face of climate change and environmental challenges. To achieve effective intelligent monitoring systems, agricultural pest and disease detection must overcome three fundamental challenges: feature degradation in dense vegetation environments, limited detection capability for sub-32×32 pixel targets, and inadequate bounding box regression for irregular pest morphologies. This study proposes YOLOv12-KMA, a novel detection framework that addresses these limitations through four synergistic architectural innovations, specifically optimized for agricultural environments. First, we introduce efficient multi-head attention (C3K2-EMA), which reduces noise interference by 41%… More >

Yuanmeng Chang, Hongmei Liu^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074204 - 12 March 2026

Abstract Driven by rapid advances in deep learning, object detection has been widely adopted across diverse application scenarios. However, in low-light conditions, critical visual cues of target objects are severely degraded, posing a significant challenge for accurate low-light object detection. Existing methods struggle to preserve discriminative features while maintaining semantic consistency between low-light and normal-light images. For this purpose, this study proposes a DL-YOLO model specially tailored for low-light detection. To mitigate target feature attenuation introduced by repeated downsampling, we design a Multi-Scale Feature Convolution (MSF-Conv) module that captures rich, multi-level details via multi-scale feature learning, More >

Burhan Baraklı, Ahmet Küçüker^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.077872 - 26 February 2026

Abstract Inspections of power transmission lines (PTLs) conducted using unmanned aerial vehicles (UAVs) are complicated by the fine structure of the lines and complex backgrounds, making accurate and efficient segmentation challenging. This study presents the Wavelet-Guided Transformer U-Net (WGT-UNet) model, a new hybrid network that combines Convolutional Neural Networks (CNNs), Discrete Wavelet Transform (DWT), and Transformer architectures. The model’s primary contribution is based on spatial and channel attention mechanisms derived from wavelet subbands to guide the Transformer’s self-attention structure. Thus, low and high frequency components are separated at each stage using DWT, suppressing structural noise and… More >