Abstract:
This paper presents an end-to-end neural network model for autonomous driving road detection. We use road boundary and deep convolutional network multi-task learning. To improve performance, we reassign the label and rebalance road pixel loss. To make the network model traffic-resistant, a road geometric transformation-based data augmentation method is suggested. A shared deep residual encoder network and multi-branch decoder sub-networks are integrated using these two novel methods. It uses road scene classification as a supervised learning task to segment and classify roads. The proposed method achieved the highest MaxF value in most road scenes in experiments. Our KITTI-Road benchmark performance is excellent.
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