Enhanced multi-scale trademark element detection using the improved DETR

Abstract

The exponential growth in the number of registered trademarks, coupled with the escalating incidents of trademark infringement, has made the automatic detection of such infractions a crucial area of study in the domain of market regulation. In light of the diverse range of elements and the pervasive presence of small targets in trademark images, we present an enhanced version of the DETR-based Multi-Scale Trademark Element Detection Network (MSTED-Net). Our primary innovation lies in incorporating a dual fusion mechanism that integrates the Spatial Attention Module (SAM) and Global Context Network (GCNet) within the backbone network, thereby providing a more robust approach to capture the essential characteristics of the trademark images under investigation. Subsequently, we develop a Multi-scale Feature Augmentation Pyramid (MFA-FPN), which aims to further fortify the model’s ability to extract features and boost the detection efficiency for small targets. The efficacy of our proposed detection network is demonstrated through experimental results, showcasing an outstanding detection accuracy of 91.12% in comparison to other state-of-the-art detection algorithms.