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Two-Layer Attention Feature Pyramid Network for small object Detection

Computer Modeling in Engineering & Sciences 2024年第10期141卷 713-731页

作者： Sheng Xiang Junhao Ma Qunli Shang Xianbao Wang Defu Chen College of Information Engineering Zhejiang University of TechnologyHangzhou310023China Binjiang Cyberspace Security Institute of ZJUT Hangzhou310056China

Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian ***,small objects are difficult to detect accurately because they contain less *** current methods,particularly those based on Feature Pyramid Network(FPN),address this challenge by leveraging multi-scale feature ***,existing FPN-based methods often suffer from inadequate feature fusion due to varying resolutions across different layers,leading to suboptimal small object *** address this problem,we propose the Two-layerAttention Feature Pyramid Network(TA-FPN),featuring two key modules:the Two-layer Attention module(TAM)and the small object detail enhancement module(SODEM).TAM uses the attention module to make the network more focused on the semantic information of the object and fuse it to the lower layer,so that each layer contains similar semantic information,to alleviate the problem of small object information being submerged due to semantic gaps between different *** the same time,SODEM is introduced to strengthen the local features of the object,suppress background noise,enhance the information details of the small object,and fuse the enhanced features to other feature layers to ensure that each layer is rich in small object information,to improve small object detection *** extensive experiments on challenging datasets such as Microsoft Common objects inContext(MSCOCO)and Pattern Analysis Statistical Modelling and Computational Learning,Visual object Classes(PASCAL VOC)demonstrate the validity of the *** results show a significant improvement in small object detection accuracy compared to state-of-theart detectors.

关键词： small object detection two-layer attention module small object detail enhancement module feature pyramid network

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