Vision Language Model for Interpretable and Fine-grained Detection of Safety Compliance in Diverse Workplaces

TL;DR

This paper presents Clip2Safety, a vision-language model framework that improves the accuracy and speed of detecting PPE compliance and attributes in diverse workplaces, enhancing safety monitoring.

Contribution

Introduction of Clip2Safety, a novel interpretable detection framework combining scene recognition, visual prompts, and fine-grained verification for PPE compliance across various scenarios.

Findings

Outperforms state-of-the-art VLMs in accuracy.

Achieves inference times 200 times faster.

Validated across six real-world workplace scenarios.

Abstract

Workplace accidents due to personal protective equipment (PPE) non-compliance raise serious safety concerns and lead to legal liabilities, financial penalties, and reputational damage. While object detection models have shown the capability to address this issue by identifying safety items, most existing models, such as YOLO, Faster R-CNN, and SSD, are limited in verifying the fine-grained attributes of PPE across diverse workplace scenarios. Vision language models (VLMs) are gaining traction for detection tasks by leveraging the synergy between visual and textual information, offering a promising solution to traditional object detection limitations in PPE recognition. Nonetheless, VLMs face challenges in consistently verifying PPE attributes due to the complexity and variability of workplace environments, requiring them to interpret context-specific language and visual cues simultaneously. We introduce Clip2Safety, an interpretable detection framework for diverse workplace safety compliance, which comprises four main modules: scene recognition, the visual prompt, safety items detection, and fine-grained verification. The scene recognition identifies the current scenario to determine the necessary safety gear. The visual prompt formulates the specific visual prompts needed for the detection process. The safety items detection identifies whether the required safety gear is being worn according to the specified scenario. Lastly, the fine-grained verification assesses whether the worn safety equipment meets the fine-grained attribute requirements. We conduct real-world case studies across six different scenarios. The results show that Clip2Safety not only demonstrates an accuracy improvement over state-of-the-art question-answering based VLMs but also achieves inference times two hundred times faster.