FCIoU: A Targeted Approach for Improving Minority Class Detection in Semantic Segmentation Systems

FCIoU: A Targeted Approach for Improving Minority Class Detection in Semantic Segmentation Systems

Blog Article

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 LNL Switch Kit infrequent minority classes.

As a solution to this problem, loss functions have been configured to take class imbalance into account and Backpack 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.