Small object detection of imbalanced traffic sign samples based on hierarchical feature fusion

Abstract

Traffic sign recognition plays a crucial role in the development of automated transportation systems, and its accuracy and real-time capabilities are fundamental to ensuring automatic driving safety. However, the size of most traffic signs is <0.5 % of the traffic scene, and the imbalanced sample distribution can limit the accuracy of recognition models. To address these problems, we propose a small-scale sensitive traffic sign detection algorithm, named GH-YOLOV5. Specifically, we present a gated enhanced module to enhance the expression of small targets, which uses a two-dimensional mask to extract the position features from the bottom to the top. We also design a hierarchical context embedding-transformer module to correctly migrate the cross-layer features of small targets to deep semantic features. Moreover, to solve the problem of extremely unbalanced categories, we put forward a new data enhancement strategy, which uses the spatial density distribution of traffic signs to synthesize samples for a small number of classes. Finally, we use a dynamic resolution strategy to compensate for small target information. The experimental results are conducted on the widely used TT100k dataset, and our results show that the proposed algorithm significantly improves the model’s ability to perceive small targets and achieves values of 90.83%, 90.32%, and 94.33% for precision, recall, and mean average precision, respectively. Therefore, we provide an efficient traffic sign recognition scheme for advanced driver assistance systems.