Traffic Lane Detection using FCN

• URL: http://arxiv.org/abs/2004.08977v1
• Date: Sun, 19 Apr 2020 22:25:12 GMT
• Title: Traffic Lane Detection using FCN
• Authors: Shengchang Zhang, Ahmed EI Koubia, Khaled Abdul Karim Mohammed
• Abstract summary: lane detection is a crucial technology that enables self-driving cars to properly position themselves in a multi-lane urban driving environments. In this project, we designed an-volutional 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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