| Autor: |
Jonathan Plangger, Mohamed Atia, Hicham Chaoui |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Machine Learning and Knowledge Extraction, Vol 5, Iss 4, Pp 1746-1759 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2504-4990 |
| DOI: |
10.3390/make5040085 |
| Popis: |
In this paper, we present a comparative study of modern semantic segmentation loss functions and their resultant impact when applied with state-of-the-art off-road datasets. Class imbalance, inherent in these datasets, presents a significant challenge to off-road terrain semantic segmentation systems. With numerous environment classes being extremely sparse and underrepresented, model training becomes inefficient and struggles to comprehend the infrequent minority classes. As a solution to this problem, loss functions have been configured to take class imbalance into account and counteract this issue. To this end, we present a novel loss function, Focal Class-based Intersection over Union (FCIoU), which directly targets performance imbalance through the optimization of class-based Intersection over Union (IoU). The new loss function results in a general increase in class-based performance when compared to state-of-the-art targeted loss functions. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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