Summary of Analyzing Mixed Construction and Demolition Waste in Material Recovery Facilities: Evolution, Challenges, and Applications Of Computer Vision and Deep Learning, by Adrian Langley et al.
Analyzing mixed construction and demolition waste in material recovery facilities: evolution, challenges, and applications of computer vision and deep learning
by Adrian Langley, Matthew Lonergan, Tao Huang, Mostafa Rahimi Azghadi
First submitted to arxiv on: 19 Sep 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 Deep learning models have shown promise in recognizing and classifying homogenous materials, but their performance for mixed, contaminated material in commercial material recycling facility settings remains understudied. This review explores recent deep learning algorithms and techniques to address this gap. The analysis synthesizes research from the past five years on deep learning for construction and demolition waste management, highlighting advancements while acknowledging limitations hindering widespread adoption. To facilitate effective integration of deep learning methodologies into waste management systems, diverse and high-fidelity datasets, advanced sensor technologies, and robust algorithmic frameworks are critical requirements. This integration is envisioned to contribute significantly towards a more sustainable and circular economic model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Construction and demolition waste needs to be recognized and sorted automatically and quickly for businesses to thrive, the environment to improve, and sustainability to succeed. Right now, deep learning models can identify certain materials well, but they struggle with mixed and contaminated waste in recycling facilities. Researchers are working on improving these models by exploring new algorithms and techniques. A review of the past five years’ worth of research found that while progress has been made, there’s still a long way to go before deep learning can be used widely in waste management. To make this happen, scientists need better data, advanced sensors, and strong computer programs. |
Keywords
* Artificial intelligence * Deep learning
