Sheng Xiang1, Junhao Ma1, Qunli Shang1, Xianbao Wang1,*, Defu Chen1,2
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 713-731, 2024, DOI:10.32604/cmes.2024.052759
Abstract Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian detection. However, small objects are difficult to detect accurately because they contain less information. Many current methods, particularly those based on Feature Pyramid Network (FPN), address this challenge by leveraging multi-scale feature fusion. However, existing FPN-based methods often suffer from inadequate feature fusion due to varying resolutions across different layers, leading to suboptimal small object detection. To address this problem, we propose the Two-layer Attention Feature Pyramid Network (TA-FPN), featuring two key modules: the Two-layer Attention Module (TAM) and the… More >
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