This paper introduces Semantic Haar-Adaptive Refined Pyramid Network
(SHARP-Net), a novel architecture for semantic segmentation. SHARP-Net
integrates a bottom-up pathway featuring Inception-like blocks with varying
filter sizes (3x3and5x5),parallelmax−pooling,andadditionalspatialdetectionlayers.Thisdesigncapturesmulti−scalefeaturesandfinestructuraldetails.Throughoutthenetwork,depth−wiseseparableconvolutionsareusedtoreducecomplexity.Thetop−downpathwayofSHARP−Netfocusesongeneratinghigh−resolutionfeaturesthroughupsamplingandinformationfusionusing1\times1and3\times3depth−wiseseparableconvolutions.WeevaluatedourmodelusingourdevelopedchallengingCulvert−SewerDefectsdatasetandthebenchmarkDeepGlobeLandCoverdataset.Ourexperimentalevaluationdemonstratedthebasemodel′s(excludingHaar−likefeatures)effectivenessinhandlingirregulardefectshapes,occlusions,andclassimbalances.Itoutperformedstate−of−the−artmethods,includingU−Net,CBAMU−Net,ASCU−Net,FPN,andSegFormer,achievingaverageimprovementsof14.4Culvert−SewerDefectsandDeepGlobeLandCoverdatasets,respectively,withIoUscoresof77.2Furthermore,theintegrationofcarefullyselectedandfine−tunedHaar−likefeaturesenhancedtheperformanceofdeeplearningmodelsbyatleast20proposedSHARP−Net,incorporatingHaar−likefeatures,achievedanimpressiveIoUof94.75featureswerealsoappliedtootherdeeplearningmodels,showinga35.0improvement,provingtheirversatilityandeffectiveness.SHARP−Netthusprovidesapowerfulandefficientsolutionforaccuratesemanticsegmentationinchallengingreal−worldscenarios.