Dual Attention U-Net with Feature Infusion: Pushing the Boundaries of
  Multiclass Defect Segmentation
Rasha Alshawi Md Tamjidul Hoque Md Meftahul Ferdaus Mahdi Abdelguerfi Kendall Niles Ken Prathak Joe Tom Jordan Klein Murtada Mousa Johny Javier Lopez
The proposed architecture, Dual Attentive U-Net with Feature Infusion (DAU-FI
Net), addresses challenges in semantic segmentation, particularly on multiclass
imbalanced datasets with limited samples. DAU-FI Net integrates multiscale
spatial-channel attention mechanisms and feature injection to enhance precision
in object localization. The core employs a multiscale depth-separable
convolution block, capturing localized patterns across scales. This block is
complemented by a spatial-channel squeeze and excitation (scSE) attention unit,
modeling inter-dependencies between channels and spatial regions in feature
maps. Additionally, additive attention gates refine segmentation by connecting
encoder-decoder pathways.
  To augment the model, engineered features using Gabor filters for textural
analysis, Sobel and Canny filters for edge detection are injected guided by
semantic masks to expand the feature space strategically. Comprehensive
experiments on a challenging sewer pipe and culvert defect dataset and a
benchmark dataset validate DAU-FI Net's capabilities. Ablation studies
highlight incremental benefits from attention blocks and feature injection.
DAU-FI Net achieves state-of-the-art mean Intersection over Union (IoU) of
95.6% and 98.8% on the defect test set and benchmark respectively, surpassing
prior methods by 8.9% and 12.6%, respectively. Ablation studies highlight
incremental benefits from attention blocks and feature injection. The proposed
architecture provides a robust solution, advancing semantic segmentation for
multiclass problems with limited training data. Our sewer-culvert defects
dataset, featuring pixel-level annotations, opens avenues for further research
in this crucial domain. Overall, this work delivers key innovations in
architecture, attention, and feature engineering to elevate semantic
segmentation efficacy.