Traffic Sign Detection Algorithm Based on Improved CenterNet

Aiming at the problem of low detection accuracy caused by the large scale change of traffic signs， this paper proposed a traffic sign detection algorithm based on improved CenterNet. ResNeSt50 was used as the backbone of improved CenterNet， and PSConv（Ploy-Scale Convolution） convolution was introduced to improve convolutional layers structure of the network. To improve the detection ability of different scale signs， this paper designed the multi-scale receptive field module which selected the appropriate expansion rate for ASPP（Atrous Spatial Pyramid Pooling） and used the attention mechanism to optimize the output of the module. The feature enhancement module was designed in the decoding network to reduce the feature loss caused by continuous up sampling. In order to inaccurate target size in CenterNet regression， GHM（Gradient Harmonizing Mechanism） was used to improve the loss function. Experimental results show that the overall accuracy of the improved algorithm is increased by 9.45%， and the detection speed reaches 91.01 frames per second， which is suitable for traffic sign detection.