The present invention discloses a robust speed-limit traffic sign detection and recognition method. The method comprises the steps of firstly, establishing a multi-feature fusion type saliency model, and subjecting each layer of the multi-feature fusion type significance model to updating and iteration to obtain a level saliency map; secondly, solving a multilayer saliency map to obtain an optimal saliency map, obtaining an ROI from the optimal saliency map, loading the obtained ROI into a super-pixel-based pre-trained CNN model to classify the ROI, and obtaining a recognition result. According to the technical scheme of the invention, traffic signs on both sides can be better highlighted through the saliency model based on a-priori position and boundary features. Meanwhile, the structural information of an image is effectively utilized through the multi-layer fusion type saliency map. Moreover, multiple small-scale detail information in a circular sign is maintained, so that a target is more complete and uniform. Therefore, the recognition efficiency and the recognition accuracy are improved.