Traffic Sign Detection Using a Multi-Scale Recurrent Attention Network

Traffic sign detection plays an important role in intelligent transportation systems. But traffic signs are still not well-detected by deep convolution neural network-based methods because the sizes of their feature maps are constrained, and the environmental context information has not been fully exploited by other researchers. What is needed is a way to incorporate relevant context detail from the neighboring layers into the detection architecture. The authors have developed a novel traffic sign detection approach based on recurrent attention for multi-scale analysis and use of local context in the image. Experiments on the German traffic sign detection benchmark and the Tsinghua-Tencent 100K data set demonstrated that their approach obtained an accuracy comparable to the state-of-the-art approaches in traffic sign detection.

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  • English

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  • Accession Number: 01729255
  • Record Type: Publication
  • Files: TLIB, TRIS
  • Created Date: Jan 29 2020 2:25PM