With　the　rapid　global　urbanization,　the　extraction　of　road　networks　has　grown　in　significance　and　complexity.　Current　research　on　road　extraction　methods　often　involves　high　computational　demands,　struggles　with　accurately　extracting　narrow　roads,　and　encounters　challenges　in　maintaining　road　connectivity.　In　this　letter,　we　introduce　a　dual-path　deep　learning　network.　The　semantic　path　integrates　insights　from　the　watershed　segmentation　algorithm　as　prior　knowledge.　Meanwhile,　the　high-resolution　path　circumvents　downsampling　during　feature　extraction,　employing　a　bilateral　edge　fusion　method　to　enhance　information　exchange　between　both　paths.　In　addition,　we　incorporate　a　large　kernel　convolution　attention　mechanism　to　enhance　the　network＇s　focus　on　roads.　Finally,　we　devise　a　strip-shaped　convolution　module　to　efficiently　capture　multiscale　information　and　reduce　the　parameter　count.　On　the　CHN-CUG　dataset,　our　method　achieves　an　intersection　over　union　(Iou)　score　of　73.36%,　surpassing　the　second-best　method　by　6.11%.　The　computational　complexity　is　measured　at　60.677　G　GFLOPS,　with　25.398　M　parameters.　Our　research　validates　the　efficacy　of　the　proposed　method　for　road　extraction　from　remote　sensing　images.