  The sewerage infrastructure is one of the most important and expensive
infrastructures in modern society. In order to efficiently manage the sewerage
infrastructure, automated sewer inspection has to be utilized. However, while
sewer defect classification has been investigated for decades, little attention
has been given to classifying sewer pipe properties such as water level, pipe
material, and pipe shape, which are needed to evaluate the level of sewer pipe
deterioration.
  In this work we classify sewer pipe defects and properties concurrently and
present a novel decoder-focused multi-task classification architecture
Cross-Task Graph Neural Network (CT-GNN), which refines the disjointed per-task
predictions using cross-task information. The CT-GNN architecture extends the
traditional disjointed task-heads decoder, by utilizing a cross-task graph and
unique class node embeddings. The cross-task graph can either be determined a
priori based on the conditional probability between the task classes or
determined dynamically using self-attention. CT-GNN can be added to any
backbone and trained end-to-end at a small increase in the parameter count. We
achieve state-of-the-art performance on all four classification tasks in the
Sewer-ML dataset, improving defect classification and water level
classification by 5.3 and 8.0 percentage points, respectively. We also
outperform the single task methods as well as other multi-task classification
approaches while introducing 50 times fewer parameters than previous
model-focused approaches. The code and models are available at the project page
http://vap.aau.dk/ctgnn
