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Traffic sign detection has received widespread concern in recent years. However, existing methods often fail to meet the real-time detection requirements, and there are many cases of missing detection in small-scale traffic sign detection. To solve these problems, a real-time traffic sign detection algorithm combining attention mechanism and contextual information is proposed. Using YOLOv5 as the base model, firstly, spatial attention mechanism is embedded in the backbone to adaptively enhance the features of important positions and suppress interference information to improve the feature extraction capability of the backbone network. Secondly, the cross stage partial window Transformer module is designed to learn correlations of different locations and to capture rich contextual information around traffic signs, which is beneficial to improving the detection accuracy of small-scale traffic signs. Thirdly, the lightweight feature fusion network is proposed to fuse the feature maps of different scales, which can reduce the computational burden and ensure the effective feature fusion. Finally, in the post-processing stage, Gaussian weighted fusion is used to amend the prediction boxes to improve the positioning accuracy. Experiments on TT100K and DFG traffic sign detection datasets show that the proposed method can effectively improve the missing detection of small-scale traffic signs, with higher accuracy and real-time performance, and can meet the requirements of traffic sign detection in actual scenarios.

With the popularity of multi-sensors, multi-modal data has received continuous attention from scientific research and industry. The technology of processing multi- source modal information through deep learning is the core. Text-to-image generation is one of the directions of multi-modal technology. Because the images generated by generative adversarial network (GAN) are more realistic, the generation of text images has made excellent progress. It can be used in many fields such as image editing and colorization, style transfer, object deformation, and photo enhancement, etc. In this review, GAN networks based on image generation function are divided into four categories: semantic- enhanced GAN, growth-able GAN, diversity-enhanced GAN, and intelligence-enhanced GAN. According to the direction provided by the taxonomy, the function-based text image generation models are integrated and compared to clarify the context. The existing evaluation indicators and commonly used data sets are analyzed, and the feasibility and future development trend of complex text processing are clarified. This review systematically complements the analysis of generative adversarial networks in text image generation and will help researchers further advance this field. [ABSTRACT FROM AUTHOR]