Augmented-Training-Aware Bisenet for Real-Time Semantic Segmentation

Abstract

Semantic segmentation techniques have become an attractive research field for autonomous driving. However, it is well-known that the computational complexity of the conventional semantic segmentation is relatively high compared to other computer vision applications. Fast inference of the semantic segmentation for autonomous driving is highly desired. A lightweight convolutional neural network, the Bilateral segmentation network (BiSeNet), is adopted in this paper. However, the performance of the conventional BiSeNet is not so reliable that the model quantization could lead to an even worse result. Therefore, we proposed an augmented training strategy to significantly improve the semantic segmentation task’s performance. First, heavy data augmentation, including CutOut, deformable distortion, and step-wise hard example mining, is used in the training phase to boost the performance of the feature representation learning. Second, the L1 and L2 norm regularization are also used in the model training to prevent the possible overfitting issue. Then, the post-quantization is performed on the TensorFlow-Lite model to significantly reduce the model size and computational complexity. The comprehensive experiments verified that the proposed method is effective and efficient for autonomous driving applications over other state-of-the-art methods.