IoU-based overlapping metric and aggregated loss function for morphological-linear neural networks

This article introduces two novel metrics based on Intersection over Union (IoU) for Morphological-Linear Neural Networks (MLNNs). The first contribution is the Overlapping Intersection over Union (OIoU) metric, which quantifies overlap among dendritic neurons in the first MLNN layer. This metric enables layer size reductions greater than 30%. The second contribution is OIoU-Loss, a cost function that integrates the OIoU metric into the training process. OIoU-Loss minimizes hyperbox overlap during training, achieving effective classification while preserving geometric separation between decision regions.