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A context- and level-aware feature pyramid network for object detection with attention mechanism

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Abstract

An object detection task includes classification and localization, which require large receptive field and high-resolution input, respectively. How to strike a balance between the two conflicting needs remains a difficult problem in this field. Fortunately, feature pyramid network (FPN) realizes the fusion of low-level and high-level features, which alleviates this dilemma to some extent. However, existing FPN-based networks overlooked the importance of features of different levels during fusion process. Their simple fusion strategies can easily cause overwritten of important information, leading to serious aliasing effect. In this paper, we propose an improved object detector based on context- and level-aware feature pyramid networks. Experiments have been conducted on mainstream datasets to validate the effectiveness of our network, where it exhibits superior performances than other state-of-the-art works.

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  1. College of Computer Science, Sichuan University, Chengdu, 610041, China

    Hao Yang & Yi Zhang

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Yang, H., Zhang, Y. A context- and level-aware feature pyramid network for object detection with attention mechanism. Vis Comput 39, 6711–6722 (2023). https://doi.org/10.1007/s00371-022-02758-x

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