LUN-BiSeNetV2: A lightweight unstructured network based on BiSeNetV2 for road scene segmentation

Abstract

With the continuous introduction of automatic driving technology, the research of road scene segmentation algorithm in machine vision has become very important. In traditional methods, most researchers use machine learning methods to segment thresholds. However, the introduction of deep learning in recent years makes convolutional neural networks widely used in this field. Aiming at the problem that the traditional threshold segmentation method is difficult to effectively extract the threshold value of road image in multiple scenes and the serious problem of over-segmentation caused by deep neural network training data directly, this paper proposes a road scene segmentation method based on a lightweight unstructured network based on BiSeNetV2. The network contains backbone segmentation network and BiSeNetV2 network. The Mobilenetv2 network is used in the backbone network to replace the Xception feature extraction network in the decoder. In addition, grouping convolution is used to replace common convolution in Mobilenetv2 network. And it selects the batch specification layer to reduce the number of parameters, without affecting the accuracy and improving the efficiency of segmentation. At the same time, due to the relatively fixed distribution position of unstructured roads in the image, attention mechanism is introduced to process advanced semantic features, so as to improve the sensitivity and accuracy of the network. The BiSeNetV2 network enhances the dominant relationship between channel features by adding a compression excitation module based on channel attention mechanism after the detail branch, so as to perceive key areas and highlight local features. The lightweight feature pyramid attention mechanism is used to optimize semantic branches, improve the feature integration between contexts, extract high-level road semantic information more efficiently and retain spatial location information to the maximum extent. Finally, local semantic features and high-level semantic features are fused to improve the effect of unstructured road detection. The experiment is trained on the open data set. The results show that compared with other state-of-theart networks, the accuracy and real-time performance of proposed LUN-BiSeNetV2 in this paper are good, and the false segmentation and edge clarity are better. Compared with the classical algorithm, the average intersection is improved by 2.2% compared with mIoU, the average pixel accuracy is improved by 7.6%, and the frame rate is improved by 24.5%.