Abstract
Detecting small signs in complex real-world environments remains challenging due to limited feature information and interference from other objects. In this article, we propose a novel text feature-guided network (TFG-Net) to improve the performance of the small signs detection not only enhancing the feature information of small signs but also avoiding the influence of other objects. As the name suggests, TFG-Net incorporates a text detection branch, which extracts additional textual features from the signs and supplies them to the object detection branch. Furthermore, the object detection branch of TFG-Net optimizes the backbone network's output structure by merging deep features and introducing a high-resolution feature layer. Finally, a fusion method that enhances both overall and local features is proposed to fully integrate detailed and semantic information. Experimental results display that our TFG-Net reaches the highest mean average precision (mAP) of 92.5% on the public datasets Tsinghua-Tencent 100K (TT100K), 83.7% on CCTSDB2021, and 79.1% on DFG, surpassing current state-of-the-art object detectors.
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