Semantic segmentation has been a complex problem in the field of computer vision and is essential for image analysis tasks. Currently, most state-of-the-art algorithms rely on deep convolutional neural networks (DCNN) to perform this task. DCNNs are able to downsample the spatial
resolution of the input image into low resolution feature mappings which are then up-sampled to produce the segmented images. However, the reduction of this spatial information causes the high frequency details of the image to be lessened resulting in blurry and inaccurate object boundaries.
In order to improve this limitation, we propose combining a DCNN used for semantic segmentation with semantic boundary information. This is done using a multi-task approach by incorporating a boundary detection network into the encoder decoder architecture SegNet. This multi-task approach
includes the addition of an edge class to the SegNet architecture. In doing so, the multi-task learning network is provided more information, thus improving segmentation accuracy, specifically boundary delineation. This approach was tested on the RGB-NIR Scene dataset. Compared to using SegNet
alone, we observe increased boundary segmentation accuracies using this approach. We are able to show that the addition of a boundary detection information significantly improves the semantic segmentation results of a DCNN.