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  • Open Access

    ARTICLE

    YOLO-SDW: Traffic Sign Detection Algorithm Based on YOLOv8s Skip Connection and Dynamic Convolution

    Qing Guo1,2, Juwei Zhang1,2,3,*, Bingyi Ren1,2

    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 >

  • Open Access

    ARTICLE

    YOLOv8s-DroneNet: Small Object Detection Algorithm Based on Feature Selection and ISIoU

    Jian Peng1, Hui He2, Dengyong Zhang2,*

    CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5047-5061, 2025, DOI:10.32604/cmc.2025.066368 - 30 July 2025

    Abstract Object detection plays a critical role in drone imagery analysis, especially in remote sensing applications where accurate and efficient detection of small objects is essential. Despite significant advancements in drone imagery detection, most models still struggle with small object detection due to challenges such as object size, complex backgrounds. To address these issues, we propose a robust detection model based on You Only Look Once (YOLO) that balances accuracy and efficiency. The model mainly contains several major innovation: feature selection pyramid network, Inner-Shape Intersection over Union (ISIoU) loss function and small object detection head. To… More >

  • Open Access

    ARTICLE

    DAFPN-YOLO: An Improved UAV-Based Object Detection Algorithm Based on YOLOv8s

    Honglin Wang1, Yaolong Zhang2,*, Cheng Zhu3

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1929-1949, 2025, DOI:10.32604/cmc.2025.061363 - 16 April 2025

    Abstract UAV-based object detection is rapidly expanding in both civilian and military applications, including security surveillance, disaster assessment, and border patrol. However, challenges such as small objects, occlusions, complex backgrounds, and variable lighting persist due to the unique perspective of UAV imagery. To address these issues, this paper introduces DAFPN-YOLO, an innovative model based on YOLOv8s (You Only Look Once version 8s). The model strikes a balance between detection accuracy and speed while reducing parameters, making it well-suited for multi-object detection tasks from drone perspectives. A key feature of DAFPN-YOLO is the enhanced Drone-AFPN (Adaptive Feature… More >

  • Open Access

    ARTICLE

    Unmanned Aerial Vehicles General Aerial Person-Vehicle Recognition Based on Improved YOLOv8s Algorithm

    Zhijian Liu*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3787-3803, 2024, DOI:10.32604/cmc.2024.048998 - 26 March 2024

    Abstract Considering the variations in imaging sizes of the unmanned aerial vehicles (UAV) at different aerial photography heights, as well as the influence of factors such as light and weather, which can result in missed detection and false detection of the model, this paper presents a comprehensive detection model based on the improved lightweight You Only Look Once version 8s (YOLOv8s) algorithm used in natural light and infrared scenes (L_YOLO). The algorithm proposes a special feature pyramid network (SFPN) structure and substitutes most of the neck feature extraction module with the Special deformable convolution feature extraction… More >

  • Open Access

    ARTICLE

    Unmanned Ship Identification Based on Improved YOLOv8s Algorithm

    Chun-Ming Wu1, Jin Lei1,*, Wu-Kai Liu1, Mei-Ling Ren1, Ling-Li Ran2

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3071-3088, 2024, DOI:10.32604/cmc.2023.047062 - 26 March 2024

    Abstract Aiming at defects such as low contrast in infrared ship images, uneven distribution of ship size, and lack of texture details, which will lead to unmanned ship leakage misdetection and slow detection, this paper proposes an infrared ship detection model based on the improved YOLOv8 algorithm (R_YOLO). The algorithm incorporates the Efficient Multi-Scale Attention mechanism (EMA), the efficient Reparameterized Generalized-feature extraction module (CSPStage), the small target detection header, the Repulsion Loss function, and the context aggregation block (CABlock), which are designed to improve the model’s ability to detect targets at multiple scales and the speed… More >

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