Authors: Rasha Alshawi, Md Meftahul Ferdaus, Mahdi Abdelguerfi, Kendall Niles, Ken Pathak, Steve Sloan
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.
Source: http://arxiv.org/abs/2408.10181v1
