Summary of Gso-yolo: Global Stability Optimization Yolo For Construction Site Detection, by Yuming Zhang et al.
GSO-YOLO: Global Stability Optimization YOLO for Construction Site Detection
by Yuming Zhang, Dongzhi Guan, Shouxin Zhang, Junhao Su, Yunzhi Han, Jiabin Liu
First submitted to arxiv on: 1 Jul 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 paper proposes a novel artificial intelligence model called Global Stability Optimization YOLO (GSO-YOLO) to improve safety monitoring on construction sites using computer vision. The model integrates two modules, the Global Optimization Module (GOM) and Steady Capture Module (SCM), to capture global contextual information and enhance detection stability. It also introduces an innovative AIoU loss function that combines CIoU and EIoU to boost detection accuracy and efficiency. Experimental results on datasets such as SODA, MOCS, and CIS demonstrate the superiority of GSO-YOLO over existing methods, achieving state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study develops a new AI system called GSO-YOLO that helps keep construction sites safe by detecting potential hazards. The system uses computer vision to analyze the site and is better at handling complex conditions than other similar systems. It also has an improved way of measuring how well it does its job, which makes it more accurate and efficient. By testing GSO-YOLO on various datasets, researchers found that it outperforms existing methods in detecting hazards on construction sites. |
Keywords
» Artificial intelligence » Loss function » Optimization » Yolo
