A semantic segmentation scheme for night driving improved by irregular convolution - Xuantao, Yang; Junying, Han; Chenzhong, Liu (Frontiers in Neurorobotics, 2023)
In order to solve the poor performance of real-time semantic segmentation of road conditions in video images due to insufficient light and motion blur when vehicles are driving at night, This study proposes a scheme: a fuzzy information complementation strategy based on generative models and a network that fuses different intermediate layer outputs to complement spatial semantics with also embeds irregular convolutional attention modules for fine extraction of motion target boundaries. First, DeblurGan is used to generate information to fix the lost semantics in the original image due to blurring; then, the outputs of different intermediate layers in the backbone network are taken out, assigned different weight scaling factors and fused; finally, by comparing the performance of different attention mechanisms, the irregular convolutional attention with the best effect is selected. The scheme achieves Global Accuracy:89.1% Mean IOU:94.2% on the night driving dataset of this experiment, which exceeds the best performance of DeepLabv3 by 1.3% and 7.2%, and achieves Accuracy:83.0% on the small volume label (Moveable), which is underperformed by all control models. The experimental results demonstrate that the solution can effectively cope with various problems faced by night driving and enhance the model's perception and analysis of driving road conditions. The results of the study provide a technical reference for the semantic segmentation problem of vehicles driving in the nighttime environment.