EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection (1806.05525v2)

Abstract: 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 more stable training due to having better discriminative information, benefiting from seeing both fake' andreal' 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 paper presents an EL-GAN approach that integrates embedding loss into adversarial training for improved lane detection.
• The method uses a DenseNet-based generator and discriminator to enforce structural consistency, reducing reliance on post-processing.
• Experimental results show a competitive 96% accuracy on the TuSimple dataset with significant reductions in false positives and negatives.

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

The paper "EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection" by Mohsen Ghafoorian et al. addresses the challenges in semantic segmentation, particularly in contexts where the desired outputs do not directly align with per-pixel classification. The authors propose an innovative application of generative adversarial networks (GANs) named EL-GAN, focusing on leveraging embedding loss to enhance the structural quality of predictions, notably in lane detection tasks for autonomous vehicles.

Overview of EL-GAN Approach

Traditional CNN-based semantic segmentation models often treat tasks as pixel-wise classification problems, which can lead to solutions that require complex post-processing to maintain high-level structures such as thinness and connectivity. This paper presents an alternative approach using GANs, specifically a variant termed EL-GAN, which incorporates an embedding loss to stabilize and improve training outcomes.

In EL-GAN, the discriminator network is exposed to both true labels and generated predictions. This enables it to capture richer, discriminative information by simultaneously processing 'real' and 'fake' outputs, leading to more stable adversarial training and better predictive quality by enforcing structural similarity directly in the output. This framework particularly benefits tasks where a pixel-wise approach falls short, such as lane marking, which involves thin and elongated structures.

Key Contributions and Findings

1. Architecture Design: The architecture utilizes a Tiramisu DenseNet generator and a DenseNet discriminator with embedding loss. The generator benefits from more robust gradient feedback by leveraging the embedding space of labels and predictions, leading to structurally more consistent results without the need for complicated post-processing or additional loss terms.
2. Training Stability: The EL-GAN methodology significantly stabilizes the adversarial training process. Empirical studies in the paper demonstrate that networks using embedding loss for training the generator exhibit less variance and higher mean performance across iterations compared to conventional GAN approaches.
3. Performance Metrics: The proposed EL-GAN model achieves a competitive 96% accuracy on the TuSimple lane detection dataset, surpassing baseline methods and validating its effectiveness. EL-GAN's performance demonstrates notable improvements in accuracy (by approximately 2%) and reductions in false positive (over 55%) and false negative (30%) rates compared to non-adversarial models.

Theoretical and Practical Implications

The introduction of EL-GAN suggests a shift in how semantic segmentation tasks, particularly those with complex structural requirements, are addressed. Embedding loss in adversarial settings opens up a pathway for creating models that inherently output predictions with high structural fidelity, reducing or eliminating the need for extensive post-training adjustments. This has implications for various applications beyond lane detection, where maintaining high-level features is critical.

Future Directions

Future research could explore extending EL-GAN to other challenging domains within semantic segmentation where structure, rather than pixel accuracy, is paramount. Additionally, studying the use of EL-GAN in a multitask setting where it could be integrated with other complementary technologies might provide further advancements in autonomous driving and beyond. Investigating the scaling of embedding loss to more complex adversarial networks and other types of neural architectures could also reveal new insights into enhancing generative model training stability and efficacy.

In conclusion, EL-GAN provides a compelling approach to addressing fundamental challenges in semantic segmentation, leveraging the power of GANs and embedding loss to produce outputs that better align with structural requirements of specific tasks. This framework not only contributes to the field of semantic segmentation but also sets a precedent for future research directions in enhancing the structural integrity of machine learning predictions.