Although previous research laid the foundation for vision-based monitoring systems using convolutional neural networks (CNNs), too little attention has been paid to the challenges associated with data imbalance and varying object sizes in far-field monitoring. To fill the knowledge gap, this paper investigates various loss functions to design a customized loss function to address the challenges. Scaffold installation operations recorded by camcorders were selected as the subject of analysis in a far-field surveillance setting. It was confirmed that the data imbalance between the workers, hardhats, harnesses, straps, and hooks caused poor performances especially for small size objects. This problem was mitigated by employing a region-based loss and Focal loss terms in the loss function of segmentation models. The findings illustrate the importance of the loss function design in improving performance of CNN models for far-field construction site monitoring.

中文翻译：

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损失函数对远场监测语义分割的影响

尽管之前的研究为使用卷积神经网络 (CNN) 的基于视觉的监控系统奠定了基础，但对远场监控中与数据不平衡和不同对象大小相关的挑战的关注太少。为了填补知识空白，本文研究了各种损失函数，以设计定制的损失函数来应对挑战。摄像机记录的脚手架安装操作被选为远场监视环境中的分析对象。经证实，工人、安全帽、安全带、带子和挂钩之间的数据不平衡导致性能不佳，尤其是对于小尺寸物体。通过在分割模型的损失函数中使用基于区域的损失和焦点损失项来缓解这个问题。