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 >

Weimin Shi^1,*, Kuntao Lv¹, Chang Xuan¹, Ji Wu²

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.075203 - 12 March 2026

Abstract The application of deep learning in fabric defect detection has become increasingly widespread. To address false positives and false negatives in fabric roll seam detection, and to improve automation efficiency and product quality, we propose the Multi-scale Context DeepLabV3+ (MSC-DeepLabV3+), a semantic segmentation network designed for fabric roll seam detection, based on DeepLabV3+. The model improvements include enhancing the backbone performance through optimization of the UIB-MobileNetV2 network; designing the Dynamic Atrous and Sliding-window Fusion (DASF) module to improve adaptability to multi-scale seam structures with dynamic dilation rates and a sliding-window mechanism; and utilizing the Progressive… 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 >

Min Liu^1,*, Zhengxiong Lu^2,*

Energy Engineering, Vol.123, No.3, 2026, DOI:10.32604/ee.2025.071144 - 27 February 2026

Abstract The accurate state of health (SOH) estimation in lithium-ion batteries represents a critical technological challenge with profound implications for electric vehicle performance and user experience. Precise SOH assessment not only enables reliable mileage prediction but also ensures operational safety. However, the complex and non-linear capacity fading process during battery cycling poses a challenge to obtaining accurate SOH. To address this issue, this study proposes an effective health factor derived from the local voltage range during the battery charging phase. First, the battery charging phase is divided evenly with reference to voltage intervals, and an importance… More >

Tianze Yu, Jianxun Zhang^*, Hongji Chen

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.073119 - 10 February 2026

Abstract Camouflaged Object Detection (COD) aims to identify objects that share highly similar patterns—such as texture, intensity, and color—with their surrounding environment. Due to their intrinsic resemblance to the background, camouflaged objects often exhibit vague boundaries and varying scales, making it challenging to accurately locate targets and delineate their indistinct edges. To address this, we propose a novel camouflaged object detection network called Edge-Guided and Multi-scale Fusion Network (EGMFNet), which leverages edge-guided multi-scale integration for enhanced performance. The model incorporates two innovative components: a Multi-scale Fusion Module (MSFM) and an Edge-Guided Attention Module (EGA). These designs… More >

Meng Wang¹, Jinghan Cai¹, Wenzheng Liu¹, Xue Yang¹, Jingjing Zhang¹, Qiangmin Zhou¹, Fanzhen Wang¹, Hang Zhang^1,*, Tonghai Liu^2,*

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2025.075541 - 30 January 2026

Abstract Tomato is a major economic crop worldwide, and diseases on tomato leaves can significantly reduce both yield and quality. Traditional manual inspection is inefficient and highly subjective, making it difficult to meet the requirements of early disease identification in complex natural environments. To address this issue, this study proposes an improved YOLO11-based model, YOLO-SPDNet (Scale Sequence Fusion, Position-Channel Attention, and Dual Enhancement Network). The model integrates the SEAM (Self-Ensembling Attention Mechanism) semantic enhancement module, the MLCA (Mixed Local Channel Attention) lightweight attention mechanism, and the SPA (Scale-Position-Detail Awareness) module composed of SSFF (Scale Sequence Feature… More >

Xiang Luo¹, Yuxuan Peng², Renghong Xie¹, Peng Li³, Yuwen Qian^3,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073700 - 12 January 2026

Abstract Deep learning has made significant progress in the field of oriented object detection for remote sensing images. However, existing methods still face challenges when dealing with difficult tasks such as multi-scale targets, complex backgrounds, and small objects in remote sensing. Maintaining model lightweight to address resource constraints in remote sensing scenarios while improving task completion for remote sensing tasks remains a research hotspot. Therefore, we propose an enhanced multi-scale feature extraction lightweight network EM-YOLO based on the YOLOv8s architecture, specifically optimized for the characteristics of large target scale variations, diverse orientations, and numerous small objects… More >

Wang Zhang^1,#, Haozhuo Cao^2,#, Qiangqiang Yao^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072948 - 12 January 2026

Abstract Recent advances in deep learning have significantly improved image deblurring; however, existing approaches still suffer from limited global context modeling, inadequate detail restoration, and poor texture or edge perception, especially under complex dynamic blur. To address these challenges, we propose the Multi-Resolution Fusion Network (MRFNet), a blind multi-scale deblurring framework that integrates progressive residual connectivity for hierarchical feature fusion. The network employs a three-stage design: (1) TransformerBlocks capture long-range dependencies and reconstruct coarse global structures; (2) Nonlinear Activation Free Blocks (NAFBlocks) enhance local detail representation and mid-level feature fusion; and (3) an optimized residual subnetwork… More >

Zhiyong Deng¹, Yanchen Ye², Jiangling Guo^1,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.069090 - 10 November 2025

Abstract With the rapid expansion of drone applications, accurate detection of objects in aerial imagery has become crucial for intelligent transportation, urban management, and emergency rescue missions. However, existing methods face numerous challenges in practical deployment, including scale variation handling, feature degradation, and complex backgrounds. To address these issues, we propose Edge-enhanced and Detail-Capturing You Only Look Once (EHDC-YOLO), a novel framework for object detection in Unmanned Aerial Vehicle (UAV) imagery. Based on the You Only Look Once version 11 nano (YOLOv11n) baseline, EHDC-YOLO systematically introduces several architectural enhancements: (1) a Multi-Scale Edge Enhancement (MSEE) module… More >

Ming Chen^1,*, Yanfei Niu², Ping Qi¹, Fucheng Wang¹

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5015-5035, 2025, DOI:10.32604/cmc.2025.067951 - 23 October 2025

Abstract This research addresses the critical challenge of enhancing satellite images captured under low-light conditions, which suffer from severely degraded quality, including a lack of detail, poor contrast, and low usability. Overcoming this limitation is essential for maximizing the value of satellite imagery in downstream computer vision tasks (e.g., spacecraft on-orbit connection, spacecraft surface repair, space debris capture) that rely on clear visual information. Our key novelty lies in an unsupervised generative adversarial network featuring two main contributions: (1) an improved U-Net (IU-Net) generator with multi-scale feature fusion in the contracting path for richer semantic feature… More >