To　solve　the　time-consuming　problem　of　manual　pavement　distress　detection　and　the　possible　low　detection　accuracy　problem　due　to　unbalanced　sample　dataset,　a　method　of　deep　data　augmentation　was　employed　to　enhance　the　dataset　of　high-definition　road　images　taken　by　smartphones.　The　results　after　the　data　augmentation　were　evaluated　and　tested　by　using　two　different　target　detection　algorithms.　The　main　research　contents　of　the　paper　included.　First,　considering　the　limitations　of　experimental　conditions　and　acquisition　environment,　a　deep　data　augmentation　method　was　employed　by　combining　WGAN-GP　and　Poisson　transfer　algorithm,　which　supplemented　and　balanced　the　training　sample　data　by　generating　road　pothole　images　under　different　lighting　conditions.　Then,　multiple　target　detection　algorithm　variants　of　Yolo(Yolov5s,　Yolov5m,　Yolov5l,　and　Yolov5x)　and　Faster　R-CNN　algorithm　were　introduced,　and　the　accuracy　and　efficiency　of　various　target　detection　algorithms　after　applying　the　data　augmentation　were　compared　through　experiments.　Experimental　results　on　the　Japanese　open　road　detection　dataset　show　that　the　average　improvement　of　P,　R　and　F1　of　five　detection　algorithms　is　2.8%,　4.0%　and　3.6%,　respectively,　after　using　the　deep　data　augmentation　method.　Among　the　five　detection　algorithms,　Yolov5l　achieved　the　highest　F1　value,　reaching　60.9%.　If　conditions　are　suitable,　such　as　in　the　test　set　with　moderate　light　conditions,　the　F1　value　of　Yolov5l　algorithm　can　reach　68.7%.　©　2022,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.