SuPEr-SAM: Using the Supervision Signal from a Pose Estimator to Train a Spatial Attention Module for Personal Protective Equipment Recognition

TL;DR

This paper introduces a deep learning approach for PPE detection that leverages pose estimation during training to enhance a spatial attention classifier, achieving accurate PPE recognition with minimal inference overhead.

Contribution

It presents a novel training method that uses pose supervision to improve a spatial attention module for PPE detection, reducing computational costs during inference.

Findings

Improved PPE detection accuracy over baseline methods

Effective use of pose supervision during training

Minimal additional computational overhead during inference

Abstract

We propose a deep learning method to automatically detect personal protective equipment (PPE), such as helmets, surgical masks, reflective vests, boots and so on, in images of people. Typical approaches for PPE detection based on deep learning are (i) to train an object detector for items such as those listed above or (ii) to train a person detector and a classifier that takes the bounding boxes predicted by the detector and discriminates between people wearing and people not wearing the corresponding PPE items. We propose a novel and accurate approach that uses three components: a person detector, a body pose estimator and a classifier. Our novelty consists in using the pose estimator only at training time, to improve the prediction performance of the classifier. We modify the neural architecture of the classifier by adding a spatial attention mechanism, which is trained using supervision signal from the pose estimator. In this way, the classifier learns to focus on PPE items, using knowledge from the pose estimator with almost no computational overhead during inference.