Impact of loss functions on semantic segmentation in far-field monitoring

Wei Chih Chern, Tam V. Nguyen, Vijayan K. Asari, Hongjo Kim

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
JournalComputer-Aided Civil and Infrastructure Engineering
Publication statusAccepted/In press - 2022

Bibliographical note

Funding Information:
This research was conducted with the support of the “2021 Yonsei University Future‐Leading Research Initiative (No. 2021‐22‐0037)” and the “National R&D Project for Smart Construction Technology (No. 22SMIP‐A158708‐03)” funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure and Transport, and managed by the Korea Expressway Corporation.

Publisher Copyright:
© 2022 Computer-Aided Civil and Infrastructure Engineering

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Computational Theory and Mathematics


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