Summary of Vision Language Model For Interpretable and Fine-grained Detection Of Safety Compliance in Diverse Workplaces, by Zhiling Chen et al.
Vision Language Model for Interpretable and Fine-grained Detection of Safety Compliance in Diverse Workplaces
by Zhiling Chen, Hanning Chen, Mohsen Imani, Ruimin Chen, Farhad Imani
First submitted to arxiv on: 13 Aug 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 proposes a novel interpretable detection framework, called Clip2Safety, to address the issue of workplace accidents due to personal protective equipment (PPE) non-compliance. The framework consists of four modules: scene recognition, visual prompt, safety items detection, and fine-grained verification. It leverages vision language models (VLMs) to recognize diverse workplace scenarios, identify necessary safety gear, detect whether required safety gear is being worn, and assess the fine-grained attributes of PPE. The framework outperforms state-of-the-art VLMs in terms of accuracy and inference time, with a speedup of two hundred times. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a big problem: workplace accidents happen when people don’t wear the right safety gear. This is a serious issue that can cause harm to people and damage to companies’ reputations. To solve this, researchers created a new system called Clip2Safety. It’s like a super smart camera that looks at what’s going on in a workplace and says “oh, you need to be wearing a hard hat here!” or “you need to wear gloves there!”. This system is really good at getting it right and can do it much faster than other systems. |
Keywords
» Artificial intelligence » Inference » Prompt
