Semisupervised and weakly supervised road detection based on generative adversarial networks

Xiaofeng Han*, Jianfeng Lu, Chunxia Zhao, Shaodi You, Hongdong Li

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    75 Citations (Scopus)

    Abstract

    Road detection is a key component of autonomous driving; however, most fully supervised learning road detection methods suffer from either insufficient training data or high costs of manual annotation. To overcome these problems, we propose a semisupervised learning (SSL) road detection method based on generative adversarial networks (GANs) and a weakly supervised learning (WSL) method based on conditional GANs. Specifically, in our SSL method, the generator generates the road detection results of labeled and unlabeled images, and then they are fed into the discriminator, which assigns a label on each input to judge whether it is labeled. Additionally, in WSL method we add another network to predict road shapes of input images and use them in both generator and discriminator to constrain the learning progress. By training under these frameworks, the discriminators can guide a latent annotation process on the unlabeled data; therefore, the networks can learn better representations of road areas and leverage the feature distributions on both labeled and unlabeled data. The experiments are carried out on KITTI ROAD benchmark, and the results show our methods achieve the state-of-the-art performances.

    Original languageEnglish
    Pages (from-to)551-555
    Number of pages5
    JournalIEEE Signal Processing Letters
    Volume25
    Issue number4
    DOIs
    Publication statusPublished - Apr 2018

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