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Understanding road scenes is a fundamental problem in advanced driver assistance systems (ADAS) for having a safe and comfortable driving. In our previous work, we proposed a novel approach to utilise the advantages of enhancement-based segmentation method to improve the road segmentation performance at reasonable computational effort. However, unpaved roads, cast shadows, grass and the side walk near the road boundary are still fuzzy and could not precisely to be identified. They suffer from either insufficient training data or the limitation of higher-order potentials in pairwise Conditional Random Field (CRF) models. To overcome these drawbacks, we propose a semi-supervised refinement strategy based on a modified cycle generative adversarial network (CycleGAN), which is more generalisable by enforcing higher-order consistency without being limited to a very specific class of high-order potentials. Proposed method uses only a fraction of annotated images, which may significantly reduce human annotation efforts. Our contribution is that unlike the existing adversarial learning methods, we proposed a modified generative model with fewer parameters than the original CycleGAN, which improves the performance while decreasing the computational cost. Moreover, we enforce cycle consistency to learn the mapping between 4-D channel unpaired images and label domain. To guarantee that the generated image from our modified network corresponds to the original image, we added the distance between a sub-set of images and their paired targeted label. The adversarial learning procedure is limited to the already predicted road boundary obtained from our recent work, which together with the limited number of annotated images boost the segmentation performance. Experiments on KITTI public road segmentation benchmark shows the effectiveness of the 4-7% of improvement with respect to our previous work based on the super pixel-CNN approach and achieves comparable performance among the top-performing algorithms of recent un/semi-supervised semantic segmentation tasks.
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