Cascade-LogoNet: Eliminating Classification Ambiguity with Cascaded Logo Detection

Due to a large number of brand logos and similar text appearances, existing deep detection networks are prone to generating multiple overlapping logos at a single location. In this paper, we propose a two-stage approach, Cascade-LogoNet, by adding a verification stage to eliminate the classification ambiguity. We group overlapping logo proposals and verify the class predictions inside each group by learning more discriminative features with strong inter-class discrepancy. We also design a lightweight detector to improve computational efficiency and prevent over-fitting given limited training data. In addition, we propose a new data augmentation strategy, named small logo preserving stitching (SLPS), to improve the detection accuracy of small logos. When stitching training images to increase the loss ratio of small logos, we keep the original small logos on the stitched image to avoid extremely undistinguishable small ones. Our Cascade-LogoNet achieves 63.5 mAP and 64.4 mAP on the dataset QMUL-OpenLogo [1] when using ResNet-50 and ResNet-50-DCN [2] as backbone respectively, which surpasses previous methods by a large margin.