Imbalance-Aware Culvert-Sewer Defect Segmentation Using an Enhanced Feature Pyramid Network
<|reference_start|>Imbalance-Aware Culvert-Sewer Defect Segmentation Using an Enhanced Feature Pyramid Network: 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.<|reference_end|>
@article{alshawi2024imbalance-aware,
    title={Imbalance-Aware Culvert-Sewer Defect Segmentation Using an Enhanced
  Feature Pyramid Network},
    author={Rasha Alshawi, Md Meftahul Ferdaus, Mahdi Abdelguerfi, Kendall Niles,
  Ken Pathak, Steve Sloan},
    journal={arXiv preprint arXiv:2408.10181},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10181},
    primaryClass={cs.CV cs.AI}
}