Abstract:

A method of traffic scene understanding was proposed using image semantic segmentation method to improve the robustness of a visual perception model in an automotive autonomous driving system. A lightweight convolutional-neural-network was designed adopting semantic segmentation using deep learning
with striking an optimal balance between efficiency and performance. The lightweight model, Mobile Net V2, was adopted in the feature-extraction section, and the convolution layers were replaced using stride = 2 with deformable convolution layers; In feature-decoder section, multi-scale Atrous deformable convolution module was designed and low-level features were also used to add more detail information. Augmented PASCAL VOC 2012 dataset was used to pre-train and evaluate the network and the traffic scene dataset, Cityscapes, was used to fine-tune and test. The results show that the new network achieves an accuracy of mean IoU (intersection over union) of 69.2%, and has better performances than that from DeepLab semantic segmentation networks with MobileNetV2. The new network takes only 127 ms per frame and 1.073 GB memory and is more efficient than that by the networks with VGG-16 and ResNet-101.

Key words: automotive autonomous driving, scene understanding, visual perception, image semantic segmentation, lightweight convolutional neural network, deep learning