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

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 article 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 two benchmark datasets show that the E-FPN outperforms state-of-the-art methods, achieving an average intersection over union (IoU) improvement of 16.2%, 27.2%, and 28.82%, 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, multiclass real-world datasets, with potential applications extending beyond culvert-sewer defect detection.