Traffic Lane Detection using FCN
This work addresses lane detection for self-driving cars, but it appears incremental as it builds on existing deep learning methods without introducing a major breakthrough.
The authors tackled the problem of automatic lane detection for self-driving cars by designing an Encoder-Decoder Fully Convolutional Network, which achieved higher accuracy than a baseline model on a real-world large-scale dataset.
Automatic lane detection is a crucial technology that enables self-driving cars to properly position themselves in a multi-lane urban driving environments. However, detecting diverse road markings in various weather conditions is a challenging task for conventional image processing or computer vision techniques. In recent years, the application of Deep Learning and Neural Networks in this area has proven to be very effective. In this project, we designed an Encoder- Decoder, Fully Convolutional Network for lane detection. This model was applied to a real-world large scale dataset and achieved a level of accuracy that outperformed our baseline model.