Road Damage Segmentation for Mobile Hardware

Abstract: The detection and early repair of road damage are paramount for the quality and safety of roads. Current detection efforts typically rely on Deep Learning methods for object detection with bounding boxes, with calculations performed on high-performance hardware. However, semantic segmentation can more accurately express the location of damages on the road, improving the descriptive quality of the detection. In addition, the use of lightweight networks to make these calculations potentially allows the technology to be run entirely on-site, without connecting to remote cloud services. The domain of road damage is inherently challenging. We select and evaluate several techniques for segmenting scarce and small objects; a common problem in traffic scene datasets containing road damage. To evaluate its effectiveness, the most promising method is applied to a new road damage dataset collected in Sweden. We present the results as an early foundation for future studies on segmenting road damage on mobile hardware.