A Cross-direction Task Decoupling Network for Small Logo Detection
This work addresses a domain-specific problem in computer vision for applications like brand monitoring, but it is incremental as it builds on existing detection methods with novel architectural tweaks.
The paper tackles the problem of detecting small logos in images, which is challenging due to their limited pixel size and aggregation, by proposing CTDNet with cross-direction feature fusion and task decoupling, achieving effective results as demonstrated on four logo datasets.
Logo detection plays an integral role in many applications. However, handling small logos is still difficult since they occupy too few pixels in the image, which burdens the extraction of discriminative features. The aggregation of small logos also brings a great challenge to the classification and localization of logos. To solve these problems, we creatively propose Cross-direction Task Decoupling Network (CTDNet) for small logo detection. We first introduce Cross-direction Feature Pyramid (CFP) to realize cross-direction feature fusion by adopting horizontal transmission and vertical transmission. In addition, Multi-frequency Task Decoupling Head (MTDH) decouples the classification and localization tasks into two branches. A multi frequency attention convolution branch is designed to achieve more accurate regression by combining discrete cosine transform and convolution creatively. Comprehensive experiments on four logo datasets demonstrate the effectiveness and efficiency of the proposed method.