Enhancing Construction Site Safety: A Lightweight Convolutional Network for Effective Helmet Detection

TL;DR

This paper develops and evaluates lightweight convolutional neural networks for helmet detection on construction sites, achieving an F1-score of 84% and laying groundwork for future improvements in safety monitoring.

Contribution

The study introduces a progressively refined CNN architecture tailored for helmet detection, demonstrating its effectiveness and identifying areas for further enhancement.

Findings

Peak F1-score of 84% achieved

Model improvements increased precision and recall

Suboptimal accuracy indicates need for further research

Abstract

In the realm of construction safety, the detection of personal protective equipment, such as helmets, plays a critical role in preventing workplace injuries. This paper details the development and evaluation of convolutional neural networks (CNNs) designed for the accurate classification of helmet presence on construction sites. Initially, a simple CNN model comprising one convolutional block and one fully connected layer was developed, yielding modest results. To enhance its performance, the model was progressively refined, first by extending the architecture to include an additional convolutional block and a fully connected layer. Subsequently, batch normalization and dropout techniques were integrated, aiming to mitigate overfitting and improve the model's generalization capabilities. The performance of these models is methodically analyzed, revealing a peak F1-score of 84\%, precision of 82\%, and recall of 86\% with the most advanced configuration of the first study phase. Despite these improvements, the accuracy remained suboptimal, thus setting the stage for further architectural and operational enhancements. This work lays a foundational framework for ongoing adjustments and optimization in automated helmet detection technology, with future enhancements expected to address the limitations identified during these initial experiments.