Summary of Enhancing Construction Site Safety: a Lightweight Convolutional Network For Effective Helmet Detection, by Mujadded Al Rabbani Alif
Enhancing Construction Site Safety: A Lightweight Convolutional Network for Effective Helmet Detection
by Mujadded Al Rabbani Alif
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 The paper presents the development and evaluation of convolutional neural networks (CNNs) for detecting personal protective equipment, specifically helmets, on construction sites. Initially, a simple CNN model showed modest results, which were improved by adding more layers, batch normalization, and dropout techniques. The models achieved a peak F1-score of 84%, precision of 82%, and recall of 86%. Although the accuracy is suboptimal, this work lays the foundation for further optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using artificial intelligence to help prevent workplace injuries by detecting helmets on construction sites. Researchers developed special kinds of computer models called neural networks to do this job. They made the models better and better by adding more parts and making some changes. The best model was able to correctly identify helmets most of the time, which is important because it can help keep workers safe. |
Keywords
» Artificial intelligence » Batch normalization » Cnn » Dropout » F1 score » Optimization » Precision » Recall
