Summary of Super-resolution Analysis For Landfill Waste Classification, by Matias Molina et al.
Super-Resolution Analysis for Landfill Waste Classification
by Matias Molina, Rita P. Ribeiro, Bruno Veloso, João Gama
First submitted to arxiv on: 2 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 study utilizes aerial imagery for environmental crime monitoring, specifically focusing on illegal landfills. By leveraging advances in artificial intelligence and computer vision, the research aims to develop models that can adapt to varying image resolutions. To evaluate waste detection algorithms, the study explores cross-domain classification and super-resolution enhancement. The results show performance improvements through image quality enhancement, but also highlight the need for careful threshold fine-tuning due to model sensitivity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study uses aerial images to monitor illegal landfills, which are a big problem because they harm the environment, economy, and people’s health. To make this work, researchers are using artificial intelligence and computer vision to develop models that can handle different image qualities. They want to see how well these models do at detecting waste in high-quality versus low-quality images. The results show that making the images better helps a little bit, but it also makes the model more sensitive and needs some adjusting. |
Keywords
» Artificial intelligence » Classification » Fine tuning » Super resolution
