Action Recognition based Industrial Safety Violation Detection

TL;DR

This paper presents an action recognition system for industrial safety that improves PPE violation detection accuracy by understanding worker actions and customizing PPE requirements, reducing false alarms.

Contribution

It introduces a novel approach combining activity recognition with object detection to enhance PPE violation detection accuracy in industrial settings.

Findings

Achieved a 23% improvement in F1-score over traditional PPE-based methods.

Effectively distinguishes worker actions to tailor PPE compliance checks.

Reduces false alarms in industrial safety monitoring.

Abstract

Proper use of personal protective equipment (PPE) can save the lives of industry workers and it is a widely used application of computer vision in the large manufacturing industries. However, most of the applications deployed generate a lot of false alarms (violations) because they tend to generalize the requirements of PPE across the industry and tasks. The key to resolving this issue is to understand the action being performed by the worker and customize the inference for the specific PPE requirements of that action. In this paper, we propose a system that employs activity recognition models to first understand the action being performed and then use object detection techniques to check for violations. This leads to a 23% improvement in the F1-score compared to the PPE-based approach on our test dataset of 109 videos.