Vision Transformer-Based Multi-Level Feature Fusion for Multi-Label Sewer Defect Classification

2026-09-10

AI Summary

研究针对大规模多标签场景下排水管道缺陷自动分类问题，提出基于视觉Transformer的多层次特征融合方法及两种轻量级架构，有效平衡了分类精度与计算复杂度。

Abstract

Automated classification of sewer defects is essential for infrastructure condition assessment and maintenance decision-making, but existing deep learning methods struggle to balance classification accuracy and computational complexity in large-scale multi-label scenarios. This study develops Sewer-Transformer-ML, a hierarchical vision Transformer with multi-level feature fusion, together with two lightweight architectures, Sewer-MobileNet-ML and Sewer-Mobile-TransNet, for resource-constrained inspection scenarios. On the Sewer-ML test set, Sewer-Transformer-ML-Base achieved an $F2_{\text{CIW}}$ of 65.68% and an $F1_{\text{Normal}}$ of 92.68%, ranking first on the public leaderboard and exceeding the second-ranked method by 7.6 percentage points in $F2_{\text{CIW}}$. Sewer-MobileNet-ML achieved an $F2_{\text{CIW}}$ of 65.73% with only 17 M parameters, representing an approximately 95% parameter reduction relative to the base model. Under the standard Sewer-Capsule data split, Sewer-Mobile-TransNet achieved 96.43% classification accuracy. When the training set was reduced to 1,177 images, pretraining on Sewer-ML consistently improved model performance. Ablation experiments further showed that direct concatenation was more effective for Transformer features, whereas attention-based fusion better supported multiscale CNN features. These findings provide a computational basis for automated sewer inspection, lightweight model design, and adaptation across civil infrastructure inspection platforms.

Problem

• sewer defects
• classification accuracy
• computational complexity
• multi-label scenarios

Innovation

• Vision Transformer
• Multi-Level Feature Fusion
• Lightweight Architecture
• Resource-Constrained Scenarios
• Attention-Based Fusion