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There are around 300, 000 kilometer of gravel roads throughout the Swedish countryside, used every day by common people and companies. These roads face constant wear due to harsh weather as well as from heavy traffic, and thus, regular maintenance is required to keep up the road standard. A cost effective maintenance requires knowledge of where support is needed and such data is obtained through inventorying. Today, the road inventory is done primarily by hand using manual tools and requiring trained personel. With new tools, this work could be partially automated which could save on cost as well as open up for more complex analysis. This project aims to investigate the possibility of automating road inventory using computer vision and machine learning. Previous works within the field show promising results using deep convolutional networks to detect and classify road anomalies like potholes and cracks on paved roads. With their results in mind, we try to translate the solutions to also work on unpaved forest roads. During the project, we have collected our own dataset containing 3522 labelled images of gravel and forest roads. There are 203 instances of potholes, 614 bare roads and 3099 snow covered roads. These images were used to train an image segmentation model based on the YOLOv8 architecture for 30 epochs. Using transfer learning we took advantage of pretrained weights gained from training on the COCO dataset. The predicted road segmentation results were also used to estimate the width of the road, using the pinhole camera model and inverse projective geometry. The segmentation model reaches a AP₅₀₋₉₅ = 0.746 for the road and 0.813 for the snow covered road. The model shows poor detection of potholes with AP₅₀₋₉₅ = 0.048. Using the road segmentations to estimate the road width shows that the model can estimate road width with a mean average error of 0.24 m.

The results from this project shows that there are already areas where machine learning could assist human operators with inventory work. Even difficult tasks, like estimating the road width of partially covered roads, can be solved with computer vision and machine learning.