Currently,　there　is　a　scarcity　of　road　disease　detection　data　sets,　single　detection　scenarios,　the　existing　road　disease　detection　methods　based　on　deep　learning　are　difficult　to　deal　with　complex　environmental　interference,　and　the　model　size　is　too　large　to　deploy.　A　multi-type　and　scenario　oriented　pavement　disease　detection　data　set　was　established　to　make　up　for　the　shortcomings　of　existing　data　sets.　Furthermore,　a　pavement　disease　detection　method　based　on　improved　YOLOv5　was　proposed.　This　method　integrated　an　attention　mechanism　and　lightweight　structural　components　to　improve　the　model　detection　accuracy　while　reducing　the　number　of　parameters,　achieving　the　detection　and　accurate　identification　of　cracks　and　potholes　pavement　damage　under　various　interference　backgrounds,　and　effectively　improving　the　aforementioned　deficiencies.　Results　show　that　the　proposed　method　has　a　high　mean　average　precison　of　93.　3%　on　the　constructed　pavement　disease　data　set,　and　the　number　of　model　parameters　is　only　about　6.　7　×106,　which　significantly　reduces　the　deployment　cost.　©　2025　Beijing　University　of　Technology.　All　rights　reserved.