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Title	A Multi-Scale Neural Network For Traffic Sign Detection Based On Pyramid Feature Maps
ID_Doc	2843
Authors	Liu J.; Zhang C.
Year	2019
Published	Proceedings - 21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019
DOI	http://dx.doi.org/10.1109/HPCC/SmartCity/DSS.2019.00255
Abstract	In the actual traffic environment, it is highly susceptible to interference from external factors such as illumination intensity and deformation of traffic signs, resulting in low detection rate. Particularly, traditional neural network only relies on features of top layer for recognition, which leads to a consequence that spatial information and edge pixels are easily lost. Aiming at the disadvantages, we combine the high-resolution pixels of shallow convolutional layers with the semantically strong features of deep convolutional layers, and propose an end-to-end feature pyramid framework to construct the complete semantic information about the target. In the optimization phase, ROI Align, soft-NMS and an improved weighted cross entropy loss function are applied to deal with the problem of the limited target pixels and unbalanced distribution of categories. Experiments on GTSDB dataset illustrate the performance of the proposed algorithm has achieved significant improvements with good generalization and stability. © 2019 IEEE.
Author Keywords	Feature extraction; Feature pyramid; Multi scale convolutional neural network; Traffic sign detection

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