SHARP-Net: A Refined Pyramid Network for Deficiency Segmentation in Culverts and Sewer Pipes
Year: 2025
Type: article
Abstract: Undetected defects in culverts and sewerpipes pose significant risks to public safety, including infrastructure collapses, flooding, and transportation disruptions. Manual inspections are time-consuming, costly, and prone to human error, while existing automated methods often struggle with real-world challenges, such as occlusions, irregular defect shapes, class imbalances, and high resource demands. This article introduces the semantic Haar-adaptive refined pyramid network (SHARP-Net), a novel architecture for semantic segmentation that delivers precise and efficient defect detection. SHARP-Net combines multiscale feature fusion, depthwise separable convolutions, and fine-tuned Haar-like features to enhance performance while reducing computational complexity. Evaluated on the culvert–sewer defects dataset and the DeepGlobe land cover dataset, SHARP-Net demonstrated superior performance. The base SHARP-Net (excluding Haar-like features) outperformed state-of-the-art methods, including U-Net, CBAM U-Net, ASCU-Net, FPN, and BiFPN, achieving an average improvement of 16.19% and 11.74% in IoU scores on the respective datasets, with IoU scores of 77.2% and 70.6% . The integration of Haar-like features further boosted performance by 2% –4% IoU, showcasing their effectiveness in capturing critical structural details across diverse datasets. By automating defect detection, SHARP-Net addresses critical infrastructure challenges, offering a scalable solution that improves safety, reduces inspection costs, and accelerates maintenance processes.
Institutions: LSU New Orleans, United States Army Corps of Engineers
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,800
Funder: U.S. Army Corps of Engineers
Award: G2864681274
OpenAlex ID: W4412972832