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 >

Hui Yu¹, Jun Liu^1,*, Mingwei Lin^2,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074191 - 10 February 2026

Abstract Small object detection has been a focus of attention since the emergence of deep learning-based object detection. Although classical object detection frameworks have made significant contributions to the development of object detection, there are still many issues to be resolved in detecting small objects due to the inherent complexity and diversity of real-world visual scenes. In particular, the YOLO (You Only Look Once) series of detection models, renowned for their real-time performance, have undergone numerous adaptations aimed at improving the detection of small targets. In this survey, we summarize the state-of-the-art YOLO-based small object detection More >

Weiguo Pan¹, Songjie Du^2,*, Bingxin Xu¹, Bin Zhang¹, Hongzhe Liu¹

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073599 - 10 February 2026

Abstract Traffic sign detection is a critical component of driving systems. Single-stage network-based traffic sign detection algorithms, renowned for their fast detection speeds and high accuracy, have become the dominant approach in current practices. However, in complex and dynamic traffic scenes, particularly with smaller traffic sign objects, challenges such as missed and false detections can lead to reduced overall detection accuracy. To address this issue, this paper proposes a detection algorithm that integrates edge and shape information. Recognizing that traffic signs have specific shapes and distinct edge contours, this paper introduces an edge feature extraction branch More >

Chiwan Ahn¹, Daehee Kim^2,*, Seongkeun Park^3,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073330 - 10 February 2026

Abstract This paper proposes a deep learning-based 3D LiDAR perception framework designed for applications such as autonomous robots and vehicles. To address the high dependency on large-scale annotated data—an inherent limitation of deep learning models—this study introduces a hybrid perception architecture that incorporates expert-driven LiDAR processing techniques into the deep neural network. Traditional 3D LiDAR processing methods typically remove ground planes and apply distance- or density-based clustering for object detection. In this work, such expert knowledge is encoded as feature-level inputs and fused with the deep network, thereby mitigating the data dependency issue of conventional learning-based… More >

Ping Fang, Mengjun Tong^*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073272 - 10 February 2026

Abstract Defect detection in printed circuit boards (PCB) remains challenging due to the difficulty of identifying small-scale defects, the inefficiency of conventional approaches, and the interference from complex backgrounds. To address these issues, this paper proposes SIM-Net, an enhanced detection framework derived from YOLOv11. The model integrates SPDConv to preserve fine-grained features for small object detection, introduces a novel convolutional partial attention module (C2PAM) to suppress redundant background information and highlight salient regions, and employs a multi-scale fusion network (MFN) with a multi-grain contextual module (MGCT) to strengthen contextual representation and accelerate inference. Experimental evaluations demonstrate More >