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  • Open Access

    ARTICLE

    A Hybrid Feature Fusion Traffic Sign Detection Algorithm Based on YOLOv7

    Bingyi Ren1,4, Juwei Zhang2,3,4,*, Tong Wang2,4

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1425-1440, 2024, DOI:10.32604/cmc.2024.052667

    Abstract Autonomous driving technology has entered a period of rapid development, and traffic sign detection is one of the important tasks. Existing target detection networks are difficult to adapt to scenarios where target sizes are seriously imbalanced, and traffic sign targets are small and have unclear features, which makes detection more difficult. Therefore, we propose a Hybrid Feature Fusion Traffic Sign detection algorithm based on YOLOv7 (HFFT-YOLO). First, a self-attention mechanism is incorporated at the end of the backbone network to calculate feature interactions within scales; Secondly, the cross-scale fusion part of the neck introduces a… More >

  • Open Access

    ARTICLE

    Infrared Small Target Detection Algorithm Based on ISTD-CenterNet

    Ning Li*, Shucai Huang, Daozhi Wei

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3511-3531, 2023, DOI:10.32604/cmc.2023.045987

    Abstract This paper proposes a real-time detection method to improve the Infrared small target detection CenterNet (ISTD-CenterNet) network for detecting small infrared targets in complex environments. The method eliminates the need for an anchor frame, addressing the issues of low accuracy and slow speed. HRNet is used as the framework for feature extraction, and an ECBAM attention module is added to each stage branch for intelligent identification of the positions of small targets and significant objects. A scale enhancement module is also added to obtain a high-level semantic representation and fine-resolution prediction map for the entire… More >

  • Open Access

    ARTICLE

    RT-YOLO: A Residual Feature Fusion Triple Attention Network for Aerial Image Target Detection

    Pan Zhang, Hongwei Deng*, Zhong Chen

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1411-1430, 2023, DOI:10.32604/cmc.2023.034876

    Abstract In recent years, target detection of aerial images of unmanned aerial vehicle (UAV) has become one of the hottest topics. However, target detection of UAV aerial images often presents false detection and missed detection. We proposed a modified you only look once (YOLO) model to improve the problems arising in object detection in UAV aerial images: (1) A new residual structure is designed to improve the ability to extract features by enhancing the fusion of the inner features of the single layer. At the same time, triplet attention module is added to strengthen the connection… More >

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