Road Detection Network Based on Anti-Disturbance and Variable-Scale Spatial Context Detector

Road detection plays a critical role in the application of smart transportation. The performance of the mainstream methods such as PSPNet (Pyramid Scene Parsing Network), DeepLab V3, FCRN (Fully Convolutional Residual Networks) still suffers from uncertain disturbances of surface abrasion buildings, pedestrians, and variation of illumination like tree-shadow. The extracted features are vulnerable to extra-disturbance, and non-local spatial-context information has not been fully utilized. In this paper, a detector based on anti-disturbance and variable-scale spatial context features (AVD) is proposed: the training of the multi-layer features of the detector is always taken under the imposing of fake-feature-disturbance from an independent generator, which is trained to exacerbate the detector errors and the mistakes of feature discriminator. The detector is prepared to be immune from the fake-feature-disturbance, and the discriminator is trained to distinguish the differences between the non-interference features and disturbing features. We also designed a novel variable-scale spatial context module to enhance the richness performance of the extracted features. And a soft connection link is bridged between the low and high feature layers. The detection experiments on the Munich road dataset and urban road dataset show that AVD is better than all the mainstream above methods. Our method increases the accuracy by 3% on the Munich remote sensing dataset and 0.4% on the urban road dataset. Our code and datasets are available at
https://github.com/Ding-Q/AVD
for download.