Volume 39 Issue 5
Nov.  2021
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SUN Xiongfeng, HUANG Zhen, CHEN Zhijun, LUO Peng. An Image Generation Method for Automated Driving Based on Improved GAN[J]. Journal of Transport Information and Safety, 2021, 39(5): 50-58,75. doi: 10.3963/j.jssn.1674-4861.2021.05.007
Citation: SUN Xiongfeng, HUANG Zhen, CHEN Zhijun, LUO Peng. An Image Generation Method for Automated Driving Based on Improved GAN[J]. Journal of Transport Information and Safety, 2021, 39(5): 50-58,75. doi: 10.3963/j.jssn.1674-4861.2021.05.007

An Image Generation Method for Automated Driving Based on Improved GAN

doi: 10.3963/j.jssn.1674-4861.2021.05.007
  • Received Date: 2021-04-12
  • There is a huge demand for driving images in the automated driving systems based on end-to-end data system. In order to solve the instability of general generative adversarial network model and the lack of diversity of generated image features when expanding the driving image data set, this work proposed an improved network model, LS-InfoGAN. The least-squares loss is used to prevent the model gradient from disappearing and alleviate the contradiction in the generator during optimization, thereby improving stability of the model. The learning ability of the generator is improved by maximizing mutual information between generated images and actual images, thus improving the diversity of its features. The transposed convolutional layer to restore the image features is used to improve the clarity of the generated image features. The effectiveness and application performance of the model are verified with a labeled image dataset acquired in self-built driving scenes. According to the academic analysis in this study, compared with the model before the improvement, the stability of the image generation process of the LS-InfoGAN model is improved by an average of 35%。Besides, when used for training in the decision network of end-to-end self-driving systems, the augmented dataset can improve the decision performance by 1% to 2% without acquiring new images. The recommended number of generated images is 1 to 2 times the number of original images when the model is used to augment the dataset.

     

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