Summary of Target Detection Of Safety Protective Gear Using the Improved Yolov5, by Hao Liu and Xue Qin
Target Detection of Safety Protective Gear Using the Improved YOLOv5
by Hao Liu, Xue Qin
First submitted to arxiv on: 12 Aug 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 YOLO-EA model enhances personal protective equipment monitoring in high-risk railway construction by integrating ECA into its convolutional layers, improving the detection of small and frequently obstructed targets. The model refines target recognition under occlusion by replacing GIoU with EIoU loss, achieving superior performance compared to YOLOv5. Specifically, YOLO-EA demonstrates 98.9% precision and 94.7% recall, outperforming YOLOv5 by 2.5% and 0.5%, respectively. Moreover, the model maintains real-time performance at 70.774 fps. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new computer model called YOLO-EA that helps keep workers safe in construction sites, like railway projects. They improved it by adding special layers that make it better at finding small things, like helmets. The model also does well when things get blocked or hidden. It even beats another similar model, YOLOv5, in detecting important safety gear. This means the new model can be used to help keep workers safe and follow safety rules. |
Keywords
» Artificial intelligence » Precision » Recall » Yolo
