Summary of A Deep Learning Approach to Detect Complete Safety Equipment For Construction Workers Based on Yolov7, by Md. Shariful Islam et al.
A Deep Learning Approach to Detect Complete Safety Equipment For Construction Workers Based On YOLOv7
by Md. Shariful Islam, SM Shaqib, Shahriar Sultan Ramit, Shahrun Akter Khushbu, Abdus Sattar, Sheak Rashed Haider Noori
First submitted to arxiv on: 11 Jun 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 A deep learning-based technique is presented for identifying safety gear worn by construction workers using the YOLO v7 object detection algorithm. A custom dataset with labeled images was used to train the model, which achieved high precision, recall, and F1-score for safety equipment recognition. The model’s evaluation produced a mAP@0.5 score of 87.7%, indicating its effectiveness in detecting safety equipment violations on building sites. This research contributes to computer vision and workplace safety by offering an automatic and trustworthy method for safety equipment detection, which can increase safety compliance and reduce accidents in the construction industry. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses artificial intelligence to help keep construction workers safe. It develops a way to recognize safety gear like helmets and goggles using pictures of workers wearing this gear. The researchers made a special dataset with labeled images to train their model, which did very well at recognizing the safety equipment. This technology can help identify when workers are not wearing their safety gear correctly, making it easier to keep them safe on the job site. |
Keywords
» Artificial intelligence » Deep learning » F1 score » Object detection » Precision » Recall » Yolo
