轻量级卷积网络提升工地安全帽检测准确率
Enhancing Construction Site Safety: A Lightweight Convolutional Network for Effective Helmet Detection
- 采用逐步优化的轻量级CNN结构，提升检测鲁棒性
- 最高达84%的F1分数，86%召回率，表现稳定
- 适合工地实时监控系统部署，兼顾精度与效率
在建筑工地安全领域，个人防护装备（如安全帽）的检测对预防工伤至关重要。本文提出并评估了用于工地现场安全帽存在性分类的卷积神经网络（CNN）。初始模型仅含一个卷积块和一个全连接层，性能有限。随后通过增加卷积块与全连接层、引入批量归一化和丢弃（dropout）技术，缓解过拟合，增强泛化能力。实验结果显示，最优配置下模型达到84%的F1分数、82%的精确率和86%的召回率。尽管性能有所提升，但准确率仍不理想，表明需进一步优化架构与训练策略。本研究为后续自动化安全帽检测技术的改进提供了基础框架。
原文摘要 · Abstract (English)
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.
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