EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection

  • Mohsen GhafoorianEmail author
  • Cedric Nugteren
  • Nóra Baka
  • Olaf Booij
  • Michael Hofmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11129)


Convolutional neural networks have been successfully applied to semantic segmentation problems. However, there are many problems that are inherently not pixel-wise classification problems but are nevertheless frequently formulated as semantic segmentation. This ill-posed formulation consequently necessitates hand-crafted scenario-specific and computationally expensive post-processing methods to convert the per pixel probability maps to final desired outputs. Generative adversarial networks (GANs) can be used to make the semantic segmentation network output to be more realistic or better structure-preserving, decreasing the dependency on potentially complex post-processing.

In this work, we propose EL-GAN: a GAN framework to mitigate the discussed problem using an embedding loss. With EL-GAN, we discriminate based on learned embeddings of both the labels and the prediction at the same time. This results in much more stable training due to having better discriminative information, benefiting from seeing both ‘fake’ and ‘real’ predictions at the same time. This substantially stabilizes the adversarial training process. We use the TuSimple lane marking challenge to demonstrate that with our proposed framework it is viable to overcome the inherent anomalies of posing it as a semantic segmentation problem. Not only is the output considerably more similar to the labels when compared to conventional methods, the subsequent post-processing is also simpler and crosses the competitive 96% accuracy threshold.



The authors would like to thank Nicolau Leal Werneck, Stefano Secondo, Jihong Ju, Yu Wang, Sindi Shkodrani and Bram Beernink for their contributions and valuable feedback.

Supplementary material

478770_1_En_15_MOESM1_ESM.pdf (91 kb)
Supplementary material 1 (pdf 91 KB)
478770_1_En_15_MOESM2_ESM.mp4 (22.1 mb)
Supplementary material 2 (mp4 22608 KB)


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mohsen Ghafoorian
    • 1
    Email author
  • Cedric Nugteren
    • 1
  • Nóra Baka
    • 1
  • Olaf Booij
    • 1
  • Michael Hofmann
    • 1
  1. 1.TomTomAmsterdamThe Netherlands

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