Road　pothole　damage　represents　a　significant　challenge　in　transportation　infrastructure.　Numerous　studies　have　aimed　to　automate　pothole　detection　by　employing　diverse　computer　vision　techniques.　There　is　a　critical　need　for　an　automated　road　pothole　detection　process　that　offers　sufficient　accuracy　and　speed,　while　also　being　cost-effective　and　straightforward　to　implement.　We　introduce　an　enhanced　road　pothole　detection　algorithm,　YOLOv5-pothole,　based　on　the　YOLOv5-seg　model.　Inputs　for　this　algorithm　include　RGB　images,　depth　images,　and　camera　position　data,　all　captured　by　a　D455　depth　camera.　Concurrently,　LiDAR　sensors　are　used　to　acquire　3D　point　cloud　data.　The　RGB　images　are　semantically　segmented　by　the　self-trained　YOLOv5-seg　network,　and　corresponding　virtual　point　clouds　are　generated　through　coordinate　transformation.　Then　we　register　the　3D　point　clouds　with　the　virtual　point　clouds　generated　from　2D　pothole　images　to　supplement　the　semantic　information　of　the　pothole.　Finally,　the　pothole　point　clouds　are　individually　segmented　using　Euclidean　clustering,　providing　the　information　of　the　pothole.　The　GRDDC2020　experimental　dataset　was　used　to　train　the　YOLOv5-seg　network.　We　tested　the　YOLOv5-pothole　algorithm　on　five　randomly　selected　road　potholes,　with　experimental　results　indicating　a　mean　detection　error　of　7.0%.　The　algorithm　demonstrated　effective　detection　across　potholes　of　varying　sizes.　This　study　offers　valuable　insights　for　future　real-time　road　pothole　detection　research.　©　2023　IEEE.