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Logo detection is a critical technology for safeguarding corporate intellectual property and maintaining cyberspace security by identifying trademark infringements and mitigating phishing risks. While contemporary logo detection algorithms have demonstrated strong performance in natural scene images, they frequently struggle with web-based content due to the structural dependencies between logo icons and surrounding textual elements. Such complexities often lead to sub-optimal detection performance. To bridge this gap, we present HyperLogoDet, a framework specifically engineered for web logo detection that integrates a text detection module with a dynamic hypergraph filtering mechanism to reduce interfering textual noise. Specifically, our approach first employs a specialized text detector to identify potential textual regions. Subsequently, dynamic hypergraph filtering is used to distinguish distracting text from text or icon components that should be preserved. Final logo identification is then performed on the refined, masked image. To evaluate the proposed framework, we introduce three WebLogo benchmarks: WebLogo-500, WebLogo-1000, and WebLogo-1500. Empirical evaluations on these benchmarks show that HyperLogoDet improves logo detection accuracy under textual interference while maintaining competitive inference efficiency. We also analyze threshold sensitivity and discuss practical limitations for future improvement.
Xia et al. (Tue,) studied this question.