面向自动驾驶数据生成的风格迁移网络研究

The data abundance of the autonomous driving dataset is the key to ensuring the robustness and reliability of autonomous driving algorithm based on deep learning, but the amount of data with night scenes and various climates and weather conditions in current autonomous driving datasets are still very limited. In order to meet the application needs in the field of unmanned driving, a style transfer network is built, which can convert the current autonomous driving data into various forms such as night and snow, etc. The network adopts a structure of single encoder-dual decoder, combined with various means such as semantic segmentation networks, skip connections, and multi-scale discriminators to improve the quality of generated images with good vision effects. Deeplabv3+ semantic segmentation network trained by real data is used to evaluate the images generated and the results show that the mean intersection over union of the images generated by the network adopted is 2.50 and 4.41 percentage points higher than that generated by AugGAN and UNIT networks with double encoder-double decoder structure respectively.