A Decoupled Cross-layer Fusion Network with Bidirectional Guidance for Detecting Small Logos

Songhui Zhao, Sujuan Hou, Baisong Zhang

Abstract: Logo detection involves the use of machine learning algorithms to recognize and locate logos in images and videos, which has applications in a wide range of industries, including e-commerce, advertising, and entertainment. However, detecting small logos is still a challenging task due to their limited coverage of pixels and unclear details resulting in insufficient feature information for detection. Therefore, they are often easily confused by complex backgrounds and have lower perturbation tolerance to the bounding box, making them more difficult to detect compared to medium and large-scale logos. To address this problem, we propose a Decoupled Cross-layer Fusion Network (DCFNet) that enhances the feature representation of small logo objects, resulting in excellent detection performance. Specifically, the proposed DCFNet first adopts a bidirectional cross-layer connection mechanism to capture complementary information between different layers. Next, a two-phase feature averaging and enhancement strategy is used to further enhance the features. In the detection phase, DCFNet decouples the classification and boundary box regression branches into two identical Fully Connected (FC) heads, improving the accuracy of small logo classification and localization by avoiding mutual interference between the branches. Extensive experiments conducted on three publicly available logo datasets demonstrate that DCFNet achieves state-of-the-art performance in detecting small logos.