Resumen
To realize the construction of smart cities, the fine management of various street objects is very important. In dealing with the form of objects, it is considered a pursuit of normativeness and precision. Store signboards are a tangible manifestation of urban culture. However, due to factors such as high spatial heterogeneity, interference from other ground objects, and occlusion, it is difficult to obtain accurate information from store signboards. In this article, in response to this problem, we propose the OSO-YOLOv5 network. Based on the YOLOv5 network, we improve the C3 module in the backbone, and propose an improved spatial pyramid pooling model. Finally, the channel and spatial attention modules are added to the neck structure. Under the constraint of rectangular features, this method integrates location attention and topology reconstruction, realizes automatic extraction of information from store signboards, improves computational efficiency, and effectively suppresses the effect of occlusion. Experiments were carried out on two self-labeled datasets. The quantitative analysis shows that the proposed model can achieve a high level of accuracy in the detection of store signboards. Compared with other mainstream object detection methods, the average precision (AP) is improved by 5.0?37.7%. More importantly, the related procedures have certain application potential in the field of smart city construction.