Enhancing Construction Site Safety: A Lightweight Convolutional Network for Effective Helmet Detection
It addresses safety for construction workers by automating helmet detection, but the work is incremental as it builds on existing CNN methods with gradual improvements.
This paper tackled the problem of detecting helmets on construction sites to improve safety by developing and refining convolutional neural networks, achieving a peak F1-score of 84%, precision of 82%, and recall of 86% with the most advanced model configuration.
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.