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SHARP-Net: A Refined Pyramid Network for Deficiency Segmentation in Culverts and Sewer Pipes

2 August 2024
Rasha Alshawi
Meftahul Ferdaus
Md Tamjidul Hoque
Kendall N. Niles
Ken Pathak
Steve Sloan
Mahdi Abdelguerfi
ArXiv (abs)PDFHTML
Abstract

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−resolutionfeaturesthroughupsamplingandinformationfusionusing and 5x5), parallel max-pooling, and additional spatial detection layers. This design captures multi-scale features and fine structural details. Throughout the network, depth-wise separable convolutions are used to reduce complexity. The top-down pathway of SHARP-Net focuses on generating high-resolution features through upsampling and information fusion using and5x5),parallelmax−pooling,andadditionalspatialdetectionlayers.Thisdesigncapturesmulti−scalefeaturesandfinestructuraldetails.Throughoutthenetwork,depth−wiseseparableconvolutionsareusedtoreducecomplexity.Thetop−downpathwayofSHARP−Netfocusesongeneratinghigh−resolutionfeaturesthroughupsamplingandinformationfusionusing1\times1and and and3\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. depth-wise separable convolutions. We evaluated our model using our developed challenging Culvert-Sewer Defects dataset and the benchmark DeepGlobe Land Cover dataset. Our experimental evaluation demonstrated the base model's (excluding Haar-like features) effectiveness in handling irregular defect shapes, occlusions, and class imbalances. It outperformed state-of-the-art methods, including U-Net, CBAM U-Net, ASCU-Net, FPN, and SegFormer, achieving average improvements of 14.4% and 12.1% on the Culvert-Sewer Defects and DeepGlobe Land Cover datasets, respectively, with IoU scores of 77.2% and 70.6%. Additionally, the training time was reduced. Furthermore, the integration of carefully selected and fine-tuned Haar-like features enhanced the performance of deep learning models by at least 20%. The proposed SHARP-Net, incorporating Haar-like features, achieved an impressive IoU of 94.75%, representing a 22.74% improvement over the base model. These features were also applied to other deep learning models, showing a 35.0% improvement, proving their versatility and effectiveness. SHARP-Net thus provides a powerful and efficient solution for accurate semantic segmentation in challenging real-world scenarios.depth−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.

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