The　detection　of　strip　steel　surface　defects　is　critical　to　ensuring　the　quality　of　strip　steel　products.　Many　deep　learning-based　methods　have　been　presented　and　can　achieve　outstanding　performance.　However,　most　of　these　methods　ignore　the　frequency　information　among　defect　areas,　which　plays　an　important　role　in　defect　detection.　This　paper　proposes　a　deep　learning　method　to　further　improve　defect　segmentation　effects　based　on　existing　methods,　called　low-pass　U-Net.　Since　most　defects　in　strip　steel　are　located　in　high-frequency　areas,　we　implement　a　low-pass　filter　before　downsampling　in　the　encoder,　which　prevents　aliasing　and　separates　out　high-frequency　information.　The　high-frequency　feature　is　transferred　into　the　decoder　to　assist　segmentation.　Following　previous　studies,　we　propose　an　adaptive　variance　Gaussian　low-pass　layer　to　generate　different　filters　according　to　each　spatial　location　of　the　feature　map,　with　lower　computing　resource　use.　Furthermore,　to　detect　defects　at　significantly　different　scales,　an　improved　Hypercolumn　module　is　adopted　at　the　end　of　the　decoder　to　upsample　and　fuse　the　feature　maps　in　different　resolutions,　where　Subpixel　replaces　the　bilinear　interpolation　to　refine　the　upsampled　results.　The　proposed　method　is　validated　on　practical　datasets　and　achieves　considerable　performance　improvement　(with　a　best　Dice　coefficient　of　0.903),　which　demonstrates　the　effectiveness　of　low-pass　U-Net.　The　introduction　of　the　adaptive　variance　Gaussian　low-pass　filter　layer　results　in　a　3%　increase　in　Dice　coefficient　in　a　comparative　inference　time,　which　achieves　a　balance　in　performance,　inference　time　and　complexity.