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

Understand

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.