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.
@article{arxiv.2408.10181,
title = {Imbalance-Aware Culvert-Sewer Defect Segmentation Using an Enhanced Feature Pyramid Network},
author = {Rasha Alshawi and Md Meftahul Ferdaus and Mahdi Abdelguerfi and Kendall Niles and Ken Pathak and Steve Sloan},
journal= {arXiv preprint arXiv:2408.10181},
year = {2024}
}