Challenges in Road Crack Segmentation Due to Coarse Annotation

Abstract

To facilitate road image data collection, participatory sensing has been proposed in the literature utilizing a dashboard camera of a normal vehicle. It is not trivial to identify road cracks in such crowdsourced images due to the dynamic natures of photographing conditions which results in inconsistent the image quality. Although previous studies presented promising ways to identify road damages using deep convolutional neural networks (CNN), the performance is insufficient to be implemented in practical monitoring purposes. This study investigates core problems in improving the road crack segmentation performance by applying state-of-the-art segmentation models based on CNN and transformer architectures. Using a benchmark dataset, it was found that coarse annotation on crowdsourced images is detrimental to the performance evaluation and further development of participatory sensing-based monitoring technology. Interestingly, segmentation models could be trained by training data with coarse annotation. This study will give a fresh insight of advancing the knowledge in participatory sensing-based infrastructure monitoring.