A semantic segmentation scheme for night driving improved by irregular convolution

In order to solve the poor performance of real-time semantic segmentation of night road conditions in video images due to insufficient light and motion blur, 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 which 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; then, the outputs of different intermediate layers are taken out, assigned different weight scaling factors, and fused; finally, the irregular convolutional attention with the best effect is selected. The scheme achieves Global Accuracy of 89.1% Mean and 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 an Accuracy of 83.0% on the small volume label (Moveable). The experimental results demonstrate that the solution can effectively cope with various problems faced by night driving and enhance the model's perception. It also provides a technical reference for the semantic segmentation problem of vehicles driving in the nighttime environment.