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

arXiv: https://arxiv.org/abs/2408.10181 · HTML: https://mlsanity.com/paper/2408.10181/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 70 references that are in the map.
- First posted on arXiv 19 Aug 2024.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Deep Metric Learning for Open World Semantic Segmentation](https://mlsanity.com/paper/2108.04562/) (2021)
- [Analyzing Overfitting under Class Imbalance in Neural Networks for Image Segmentation](https://mlsanity.com/paper/2102.10365/) (2021)
- [An Image is Worth More Than 16x16 Patches: Exploring Transformers on Individual Pixels](https://mlsanity.com/paper/2406.09415/) (2024)
- [A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation](https://mlsanity.com/paper/2304.02858/) (2023)
- [Feature Pyramid Network for Multi-Class Land Segmentation](https://mlsanity.com/paper/1806.03510/) (2018)
- [Explaining Deep Models through Forgettable Learning Dynamics](https://mlsanity.com/paper/2301.04221/) (2023)
- [Dual Attention U-Net with Feature Infusion: Pushing the Boundaries of Multiclass Defect Segmentation](https://mlsanity.com/paper/2312.14053/) (2023)
- [Large-kernel Attention for Efficient and Robust Brain Lesion Segmentation](https://mlsanity.com/paper/2308.07251/) (2023)
- [Deep learning based on Transformer architecture for power system short-term voltage stability assessment with class imbalance](https://mlsanity.com/paper/2310.11690/) (2023)

## Built on by (most cited first, 7 of 7)

- [DeltaSeg: Tiered Attention and Deep Delta Learning for Multi-Class Structural Defect Segmentation](https://mlsanity.com/paper/2604.18745/) (2026)
- [Multi-Conditioned Diffusion Synthesis of Sand Boils for Low-Resource Earthen-Levee Inspection](https://mlsanity.com/paper/2607.08794/) (2026)
- [KARMA: Efficient Structural Defect Segmentation via Kolmogorov-Arnold Representation Learning](https://mlsanity.com/paper/2508.08186/) (2025)
- [Attention-Enhanced Prototypical Learning for Few-Shot Infrastructure Defect Segmentation](https://mlsanity.com/paper/2510.05266/) (2025)
- [VeloxNet: Efficient Spatial Gating for Lightweight Embedded Image Classification](https://mlsanity.com/paper/2603.19496/) (2026)
- [EfficientPENet: Real-Time Depth Completion from Sparse LiDAR via Lightweight Multi-Modal Fusion](https://mlsanity.com/paper/2604.18790/) (2026)
- [Leak-Free Cross-Validated Stacking with Per-Architecture Calibration for Sand-Boil Segmentation in Earthen Levees](https://mlsanity.com/paper/2607.25367/) (2026)

## Abstract (arXiv)

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.