Summary of Stridenet: Swin Transformer For Terrain Recognition with Dynamic Roughness Extraction, by Maitreya Shelare et al.
StrideNET: Swin Transformer for Terrain Recognition with Dynamic Roughness Extraction
by Maitreya Shelare, Neha Shigvan, Atharva Satam, Poonam Sonar
First submitted to arxiv on: 20 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 proposed StrideNET model combines the strengths of convolutional neural networks (CNNs) and vision transformers for remote-sensing image classification tasks. By leveraging the self-attention mechanism in transformer-based models, StrideNET captures global relationships and long-range dependencies between image patches, enabling accurate terrain recognition and surface roughness extraction. The terrain recognition branch employs the Swin Transformer to classify varied terrains, while the roughness extraction branch uses statistical texture-feature analysis techniques. Trained on a custom dataset with four terrain classes (grassy, marshy, sandy, and rocky), StrideNET achieves an average test accuracy of over 99% across all classes, outperforming benchmark CNN and transformer-based models. This work has potential applications in environmental monitoring, land use and cover classification, disaster response, and precision agriculture. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary StrideNET is a new model that helps computers identify different types of terrain from satellite images. It uses special algorithms to learn patterns in the images and can tell apart things like grassy areas, marshes, sandy beaches, and rocky mountains. This model works better than previous models at doing this job, which could be important for things like monitoring the environment, understanding how people use land, responding to natural disasters, and growing crops more efficiently. |
Keywords
» Artificial intelligence » Classification » Cnn » Image classification » Precision » Self attention » Transformer
