Title: Application of Decision Rules for Handling Class Imbalance in Semantic Segmentation,
Abstract: As part of autonomous car driving systems, semantic segmentation is an
essential component to obtain a full understanding of the car's environment.
One difficulty, that occurs while training neural networks for this purpose, is
class imbalance of training data. Consequently, a neural network trained on
unbalanced data in combination with maximum a-posteriori classification may
easily ignore classes that are rare in terms of their frequency in the dataset.
However, these classes are often of highest interest. We approach such
potential misclassifications by weighting the posterior class probabilities
with the prior class probabilities which in our case are the inverse
frequencies of the corresponding classes in the training dataset. More
precisely, we adopt a localized method by computing the priors pixel-wise such
that the impact can be analyzed at pixel level as well. In our experiments, we
train one network from scratch using a proprietary dataset containing 20,000
annotated frames of video sequences recorded from street scenes. The evaluation
on our test set shows an increase of average recall with regard to instances of
pedestrians and info signs by $25\%$ and $23.4\%$, respectively. In addition,
we significantly reduce the non-detection rate for instances of the same
classes by $61\%$ and $38\%$.