According　to　the　latest　statistics　at　the　end　of　2022,　the　total　length　of　highways　in　China　has　reached　5.3548　million　kilometers,　with　a　maintenance　mileage　of　5.3503　million　kilometers,　accounting　for　99.9%　of　the　total　maintenance　coverage.　Relying　on　inefficient　manual　pavement　detection　methods　is　difficult　to　meet　the　needs　of　large-scale　detection.　To　tackle　this　issue,　experiments　were　conducted　to　explore　deep　learning　-based　intelligent　identification　models,　leveraging　pavement　distress　data　as　the　fundamental　basis.　The　dataset　encompasses　pavement　micro　cracks,　which　hold　particular　significance　for　the　purpose　of　pavement　preventive　maintenance.　The　two　-stage　model　Faster　R　-CNN　achieved　a　mean　average　precision　(mAP)　of　0.938,　which　surpassed　the　one　-stage　object　detection　algorithms　YOLOv5　(mAP:　0.91)　and　YOLOv7　(mAP:　0.932).　balance　model　weight　and　detection　performance,　this　study　proposes　a　YOLO-based　optimization　method　on　the　basis　of　YOLOv5.　This　method　achieves　comparable　detection　performance　(mAP:　0.93)　to　that　of　two　-stage　detectors,　while　exhibiting　only　a　minimal　increase　in　the　number　parameters.　Overall,　the　two　-stage　model　demonstrated　excellent　detection　performance　when　using　residual　network　(ResNet)　as　the　backbone,　whereas　the　YOLO　algorithm　of　the　one　-stage　detection　model　proved　to　be　more　suitable　for　practical　engineering　applications.