Imbalance-Aware Culvert-Sewer Defect Segmentation Using an Enhanced Feature Pyramid Network

TL;DR

This paper presents E-FPN, an advanced deep learning model designed to improve segmentation accuracy in imbalanced datasets for infrastructure inspection, outperforming existing methods with innovative architectural features and data strategies.

Contribution

The paper introduces E-FPN with architectural enhancements and imbalance handling strategies, achieving significant improvements over state-of-the-art segmentation models.

Findings

E-FPN achieves 13.8% and 27.2% IoU improvements on two datasets.

Class decomposition and data augmentation increase IoU by 6.9%.

E-FPN effectively handles multi-class, imbalanced real-world datasets.

Abstract

Imbalanced datasets are a significant challenge in real-world scenarios. They lead to models that underperform on underrepresented classes, which is a critical issue in infrastructure inspection. This paper introduces the Enhanced Feature Pyramid Network (E-FPN), a deep learning model for the semantic segmentation of culverts and sewer pipes within imbalanced datasets. The E-FPN incorporates architectural innovations like sparsely connected blocks and depth-wise separable convolutions to improve feature extraction and handle object variations. To address dataset imbalance, the model employs strategies like class decomposition and data augmentation. Experimental results on the culvert-sewer defects dataset and a benchmark aerial semantic segmentation drone dataset show that the E-FPN outperforms state-of-the-art methods, achieving an average Intersection over Union (IoU) improvement of 13.8% and 27.2%, respectively. Additionally, class decomposition and data augmentation together boost the model's performance by approximately 6.9% IoU. The proposed E-FPN presents a promising solution for enhancing object segmentation in challenging, multi-class real-world datasets, with potential applications extending beyond culvert-sewer defect detection.