Study on Optimized Lane Detection Algorithm based on U-Net

摘要

Lane detection has always been one of the important researches in semantic segmentation, but there are many problems in traditional lane detection algorithms, such as the much larger image pixels, the poor detection effect and so on. Based on the U-Net semantics segmentation network model, this paper redesigns two U-Net optimization network models based on RESNET residual module, and puts forward a series of image preprocessing methods aiming at the dataset's much larger pixels and some other problems. In the training process, the training data are adjusted Besides, date cleaning, data enhancement, data exposure and other operations are added. The final training model performs well on Apollos capes dataset.