Robust Signboard Detection and Recognition in Real Environments

The detection and recognition of signboards have become increasingly important in the consumer electronics industry due to its wide range of potential applications. These applications include aiding visually impaired consumers in navigating through unfamiliar areas, identifying location landmarks for wayfinding, and providing targeted advertising and marketing services to consumers. However, the accuracy of signboard detection remains challenging due to the diversity of designs, which may incorporate text and images, and the complexity of environments, such as occlusion, shooting angles, and lighting conditions. Most existing detection methods struggle to distinguish small and similar signboards. In this paper, we propose robust signboard detection and recognition based on template generation. We also collected a new dataset that contains about 30,000 images, in 14 categories of signboards in Taiwan for training and free public use. The proposed method is a one-stage detector, which utilizes multi-scale features in the Darknet-19 network to learn object features effectively, detecting tiny and large objects. In addition, the proposed template generation method was designed to improve the overall accuracy. We compare our results with the Yolo series models. The results show that our proposed method more efficiently detects and recognizes signboards, achieving an mAP score of 95.99%, total parameters of 62.7M, and FPS of 8.3.