TY  - CONF
AU  - Joakim Bruslund Haurum
AU  - Meysam Madadi
AU  - Sergio Escalera
AU  - Thomas B. Moeslund
A2  - WACV
PY  - 2022//
TI  - Multi-Task Classification of Sewer Pipe Defects and Properties Using a Cross-Task Graph Neural Network Decoder
BT  - Winter Conference on Applications of Computer Vision
SP  - 2806
EP  - 2817
KW  - Vision Systems
KW  - Applications Multi-Task Classification
N2  - 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 sewerdefect 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-toend 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 andwater 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 modelfocused approaches.
UR  - https://ieeexplore.ieee.org/document/9706846
L1  - http://refbase.cvc.uab.es/files/BME2022.pdf
UR  - http://dx.doi.org/10.1109/WACV51458.2022.00151
N1  - HUPBA; no proj
ID  - Joakim Bruslund Haurum2022
ER  -