Chanchan Zhao^1,#, Xiaoyu Gao^1,#, Bao Shi^2,*, Ziyang Zhang¹

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085282 - 13 August 2026

Abstract Detecting objects in unmanned aerial vehicle (UAV) imagery is challenging because most targets occupy only a small number of pixels and are often distributed in crowded regions with cluttered backgrounds. For edge-side UAV applications, the detector must also remain compact enough for real-time inference on low-power computing platforms. To meet these requirements, this study develops a YOLOv11n-based small-object detector by redesigning feature extraction, cross-scale fusion, and prediction modules. In the backbone, the proposed Dual-Context Large-Small Convolution (DCLSConv) is embedded into the C3k2 structure to form C3k2-DC, allowing the network to capture broader contextual cues while… More >

Heng Wang¹, Shichao Li¹, Long Xu^2,*, Chuqiao Wang¹, Yanzhou Feng¹, Zou Zhou^1,3,4,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083432 - 13 August 2026

Abstract The inherent challenges of small objects in remote sensing imagery encompass the degradation of fine-grained spatial details throughout the downsampling stages, semantic inconsistency during multi-level feature fusion, along with unreliable localization caused by noisy samples. To address these issues, this paper proposes an efficient small-object detector termed EMW-YOLO. An Efficient Down-sampling (EDS) module is introduced to preserve fine-grained spatial information and enhance feature representation during feature extraction through spatial rearrangement and cross-dimensional attention. A Multi-Scale Fusion and Enhancement (MSFE) architecture is further developed to improve semantic consistency across feature levels by combining local enhancement with More >

Abdulhamid Victor Ibrahim, Haoyuan Li, Bingyang Guo, Ruiyun Yu^*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083186 - 13 August 2026

Abstract The detection of small objects in low-resolution aerial imagery presents a persistent challenge in computer vision, where hardware constraints, imaging altitude, and scene complexity collectively degrade spatial detail to the point where standard detection frameworks fail. Existing super-resolution methods offer partial remedies but are limited by substantial computational costs and by feature discrepancies between Generative Adversarial Network-enhanced and real high-resolution images that degrade downstream detection accuracy. This paper presents YOLO-Flex, a unified framework that addresses these challenges through the co-design of a super-resolution module and a task-adapted object detection network, jointly optimized through a shared… More > Graphic Abstract

Jordán Pascual Espada¹, Lucía Alonso Virgós^2,*, Juan Luis Carús³, Miguel Ángel Fernández Fernández³

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.077447 - 13 August 2026

Abstract This research focuses on multi-object detection for interrelated industrial components, such as substation parts. Traditional vision models like YOLO rely primarily on visual features, which limits their ability to: (1) disambiguate visually similar objects using contextual cues like relative position or size, and (2) reinforce low-confidence detections that are spatially plausible given neighboring elements. For instance, a visually similar but semantically incorrect object may be misclassified, or a valid but partially occluded component may be discarded due to a low appearance-based score. Our approach addresses these issues by integrating formalized rules based on relative positions… More >

Tianqi Wang, Yang Li, Zhisong Pan^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083923 - 23 July 2026

Abstract Existing knowledge distillation methods for object detection struggle to bridge the teacher-student capacity gap and overlook the inherent differences between classification and regression subtasks. To address these issues, we propose a Bridging Multi-dimensional Gaps Knowledge Distillation (BMGKD) method, which comprises two core modules: a feature difference distillation module and a response difference distillation module. The feature difference distillation module achieves global feature structural alignment via improved centered kernel alignment and performs local key feature alignment using joint spatial and channel-wise cosine similarity masks. The response difference distillation module constructs a dynamic classification mask and a… More >

Muhammad Asim^1,#, Muhammad Amin Shahid^1,#, Abdullah Khan¹, Muhammad Ishaq¹, Jawad Khan^2,*, Syed Qamrun Nisa³, Muhammad Amir Khan⁴, Ines Hilali Jaghdam⁵

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083596 - 23 July 2026

Abstract Indoor object detection presents unique challenges such as occlusions, varying lighting conditions, and cluttered environments. While several object detection frameworks, including RetinaNet, Faster R-CNN, SSD, and EfficientDet, have been proposed, they often suffer from high computational cost, reduced inference speed, and limited accuracy in terms of mean Average Precision (mAP), particularly in real-time scenarios. In this study, lightweight YOLO variants, namely YOLOv7, YOLOv8s, YOLOv9s, and a fine-tuned YOLOv9s which considers the optimized training strategy based on albumentations. All the models are evaluated for indoor object detection using the RGB TUT Indoor dataset. The models are… More >

Linyu Dong¹, Tao Li², Hao Li^2,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083042 - 23 July 2026

Abstract While Transformer-based detectors excel in global modeling, their efficacy in unmanned aerial vehicle (UAV)-based tiny object detection is limited by information loss during aggressive downsampling and the lack of high-frequency structural cues. To bridge this gap, we propose HiFreq-DETR, a dedicated framework that optimizes the synergy between spatial fidelity and semantic discriminability. The core innovation lies in its hierarchical information preservation strategy, which employs a ResNeSt14d backbone coupled with an S2 spatial injection path to recover critical high-resolution structural anchors, and introduces a frequency-selective interaction module to decouple target saliency from background noise. Experimental results More >

Shaohuang Bian^1,#, Qinxiu Gao^1,#, Shan Su¹, Weifeng Wang¹, Feng Huang^2,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081818 - 23 July 2026

Abstract Tomato, as a globally important crop, its freshness directly affects postharvest quality, market value, and consumer acceptance. Traditional tomato freshness evaluation mainly relies on manual inspection and experience-based judgment, which is time-consuming, labor-intensive, and inefficient. Meanwhile, plasma technology has shown promising potential in agricultural preservation due to its safety and effectiveness, making the evaluation of tomato freshness after plasma treatment particularly important. In recent years, with the rapid development of deep learning technology, non-destructive detection methods based on image analysis have become important tools for agricultural product quality assessment. This study proposes an improved YOLOv8n-based… More >

Junchen Huo¹, Song Wang^1,*, Enqing Chen¹, Yingqiang Ding¹, Shouyi Yang²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081658 - 23 July 2026

Abstract Multi-modal 3D object detection, which leverages the complementary strengths of LiDAR point clouds and camera RGB images, has emerged as a critical component of 3D perception in autonomous driving. As a critical challenge in multi-modal learning, modality alignment aims to establish accurate semantic correspondences across distinct modalities. However, existing methods encounter significant difficulties in achieving robust alignment when data from one modality is obscured, such as in the presence of object occlusion or adverse environmental conditions, including illumination variations and inclement weather. To alleviate this issue, we present CG-MAE, a dual-branch Bird’s-Eye-View (BEV) masked autoencoder… More >

Joonhyeok Moon¹, Siheon Jeong¹, Byeonghyun Lee¹, Jeik Choi¹, Ki-Yong Oh^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081475 - 30 June 2026

Abstract Maintaining overhead distribution facilities inherently involves high risks for operators, where ensuring worker safety and operational efficiency remains a paramount challenge. In particular, automating the positioning of aerial work platforms is crucial to mitigate electrocution hazards during live-line maintenance tasks. This paper proposes a novel autonomous framework for detecting jumper lines that could be employed to estimate the optimal bucket position in live-line maintenance of overhead distribution systems. The proposed framework comprises three core modules to form a unified pipeline for autonomous field inspection: a 4D multi-modal map, Sparse-dense fusion network (SDFNet), and Rotational multi-pyramid… More >