Lane Road Segmentation Based on Improved UNet Architecture for Autonomous Driving
Hoang Tran Ngoc, Huynh Vu Nhu Nguyen, Khang Hoang Nguyen, Luyl-Da Quach
Abstract
This paper introduces a real-time workflow for implementing neural networks in the context of autonomous driving. The UNet architecture is specifically selected for road segmentation due to its strong performance and low complexity. To further improve the model's capabilities, Local Binary Convolution (LBC) is incorporated into the skip connections, enhancing feature extraction, and elevating the Intersection over Union (IoU) metric. The performance evaluation of the model focuses on road detection, utilizing the IOU metric. Two datasets are used for training and validation: the widely used KITTI dataset and a custom dataset collected within the ROS2 environment. Simulation validation is performed on both datasets to assess the performance of our model. The evaluation of our model on the KITTI dataset demonstrates an impressive IoU score of 97.90% for road segmentation. Moreover, when evaluated on our custom dataset, our model achieves an IoU score of 98.88%, which is comparable to the performance of conventional UNet models. Our proposed method to reconstruct the model structure and provide input feature extraction can effectively improve the performance of existing lane road segmentation methods.