Lane detection via disentangled representation network with slope consistency loss

Existing works in lane detection focus on learning the general robust representation across different scenarios to overcome the impact of the lack of visual cues. However, factors leading to the absence of visual cues vary across different scenarios and the training data from challenging conditions is relatively small compared to common conditions. These problems result in the inability of existing methods to maintain robust lane detection in different scenarios for practical applications. To address these problems, this work presents a novel Disentangled Representation Network called DRNet, which disentangles the lane feature representations using a disentangled representation network to efficiently learn the lane representations corresponding to the specific condition. Meanwhile, DRNet also mitigates the adverse effects of data imbalance. Specifically, we disentangle lane representation via five branches, respectively to the common scenes, crowded objects, low light, dazzle light and other conditions. Due to the separated model of different conditions, each branch can be represented using a small number of parameters, which can be sufficiently learned using corresponding training subset. Moreover, existing works perform lane classification or regression using pixel-level losses, which neglect the important shape information. To this end, we design a novel slope consistency loss to take both global and local slope consistencies between prediction and ground truth into account for lane detection, which can adaptively adjust the lane shape and location. Extensive experiments on the CULane and TuSimple datasets show that our DRNet outperforms state-of-the-art methods, as it can reach 81.07% F1 on CULane and 97.97% on TuSimple.