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Logo detection plays an integral role in many applications. However, handlingsmall logos is still difficult since they occupy too few pixels in the image,which burdens the extraction of discriminative features. The aggregation ofsmall logos also brings a great challenge to the classification andlocalization of logos. To solve these problems, we creatively proposeCross-direction Task Decoupling Network (CTDNet) for small logo detection. Wefirst introduce Cross-direction Feature Pyramid (CFP) to realizecross-direction feature fusion by adopting horizontal transmission and verticaltransmission. In addition, Multi-frequency Task Decoupling Head (MTDH)decouples the classification and localization tasks into two branches. A multifrequency attention convolution branch is designed to achieve more accurateregression by combining discrete cosine transform and convolution creatively.Comprehensive experiments on four logo datasets demonstrate the effectivenessand efficiency of the proposed method.

A Cross-direction Task Decoupling Network for Small Logo Detection