Evaluating Cascaded Methods of Vision-Language Models for Zero-Shot Detection and Association of Hardhats for Increased Construction Safety

TL;DR

This paper assesses vision-language models, especially OWLv2, for zero-shot hardhat detection in construction images, proposing a new dataset and cascaded detection method to improve safety monitoring.

Contribution

Introduces a new Hardhat Safety Detection Dataset and a cascaded detection approach using foundation models for zero-shot hardhat detection.

Findings

OWLv2 achieves 0.6493 average precision in hardhat detection

Created a comprehensive benchmark dataset for construction safety

Analyzed limitations and potential improvements for real-world deployment

Abstract

This paper evaluates the use of vision-language models (VLMs) for zero-shot detection and association of hardhats to enhance construction safety. Given the significant risk of head injuries in construction, proper enforcement of hardhat use is critical. We investigate the applicability of foundation models, specifically OWLv2, for detecting hardhats in real-world construction site images. Our contributions include the creation of a new benchmark dataset, Hardhat Safety Detection Dataset, by filtering and combining existing datasets and the development of a cascaded detection approach. Experimental results on 5,210 images demonstrate that the OWLv2 model achieves an average precision of 0.6493 for hardhat detection. We further analyze the limitations and potential improvements for real-world applications, highlighting the strengths and weaknesses of current foundation models in safety perception domains.