Summary of Sharp-net: a Refined Pyramid Network For Deficiency Segmentation in Culverts and Sewer Pipes, by Rasha Alshawi et al.
SHARP-Net: A Refined Pyramid Network for Deficiency Segmentation in Culverts and Sewer Pipes
by Rasha Alshawi, Md Meftahul Ferdaus, Md Tamjidul Hoque, Kendall Niles, Ken Pathak, Steve Sloan, Mahdi Abdelguerfi
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Semantic Haar-Adaptive Refined Pyramid Network (SHARP-Net), a novel architecture for semantic segmentation that integrates bottom-up and top-down pathways to capture multi-scale features and fine structural details. The model uses depth-wise separable convolutions, upsampling, and information fusion to generate high-resolution features. SHARP-Net is evaluated using the Culvert-Sewer Defects and DeepGlobe Land Cover datasets, outperforming state-of-the-art methods such as U-Net, CBAM U-Net, ASCU-Net, FPN, and SegFormer. The model achieves average improvements of 14.4% and 12.1%, respectively, with IoU scores of 77.2% and 70.6%. The training time is reduced, and the integration of Haar-like features enhances the performance of deep learning models by at least 20%. SHARP-Net provides a powerful and efficient solution for accurate semantic segmentation in challenging real-world scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to do something called semantic segmentation. It’s like recognizing objects in pictures, but it’s more complicated because the objects can be broken or hidden. The researchers made a new model called SHARP-Net that works better than other models on this task. They tested it on two different datasets and showed that it was much better at finding things correctly. |
Keywords
» Artificial intelligence » Deep learning » Semantic segmentation
