Impact of loss functions on semantic segmentation in far‐field monitoring
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 i...
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| Vydáno v: | Computer-aided civil and infrastructure engineering Ročník 38; číslo 3; s. 372 - 390 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Hoboken
Wiley Subscription Services, Inc
01.02.2023
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| Témata: | |
| ISSN: | 1093-9687, 1467-8667 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | 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. |
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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 1093-9687 1467-8667 |
| DOI: | 10.1111/mice.12832 |