This　study　introduces　a　visual　severity　grading　method　for　pavement　cracks　by　using　high-resolution　images　as　inputs.　The　method　incorporates　a　light-weight,　fast　crack　segmentation　network　(CS-Net)　to　accurately　extract　cracks　from　images,　and　measure　their　lengths　and　widths　with　an　image　metric-skeleton　model.　Additionally,　a　five-level　severity　grading　system　is　proposed　to　delineate　the　severity　of　crack　lengths　and　widths.　The　CS-Net　performs　better　than　existing　state-of-the-art　networks　on　experiments,　with　F-1-score,　intersection　over　union　(IoU),　frames　per　second　(FPS),　and　model　sizes　of　78.66%,　65.25%,　55.00fps　and　1.66MB,　respectively.　The　metric-skeleton　model　precisely　measures　the　crack　with　an　overall　mean　error　of　approximately　0.30　pixels.　According　to　maintenance　guidelines,　the　distress　grading　level　of　pavement　cracks　is　correspondingly　assigned　based　on　the　length,　width,　and　area　values.　The　study　findings　can　guide　in　allocating　the　maintenance　budget　for　fixing　highly　defective　road　sections.