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Traffic sign detection is a vital task in the visual system of self-drivingcars and the automated driving system. Recently, novel Transformer-based modelshave achieved encouraging results for various computer vision tasks. We stillobserved that vanilla ViT could not yield satisfactory results in traffic signdetection because the overall size of the datasets is very small and the classdistribution of traffic signs is extremely unbalanced. To overcome thisproblem, a novel Pyramid Transformer with locality mechanisms is proposed inthis paper. Specifically, Pyramid Transformer has several spatial pyramidreduction layers to shrink and embed the input image into tokens with richmulti-scale context by using atrous convolutions. Moreover, it inherits anintrinsic scale invariance inductive bias and is able to learn local featurerepresentation for objects at various scales, thereby enhancing the networkrobustness against the size discrepancy of traffic signs. The experiments areconducted on the German Traffic Sign Detection Benchmark (GTSDB). The resultsdemonstrate the superiority of the proposed model in the traffic sign detectiontasks. More specifically, Pyramid Transformer achieves 77.8% mAP on GTSDB whenapplied to the Cascade RCNN as the backbone, which surpasses most well-knownand widely-used state-of-the-art models.

Pyramid Transformer for Traffic Sign Detection