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Image segmentation often faces a trade-off between using higher resolution images to detect fine details, such as the edges of objects or thin structures, and lower resolution images which is more suitable for accurate segmentation of massive objects. Because low resolution images require less resources, accurate detection of small objects is often less prioritized in trying to achieve the highest accuracy. In this paper, we propose to improve the segmentation of small and thin objects by convolutional neural networks by adding a morphological element to the loss function used for training the segmentation network. The approach is tested on a traffic sign segmentation problem using the Cityscapes dataset with a training set of 2 979 images and is shown to have an advantage over the popular cross-entropy (CE) and a ground truth (GT) affinity map weighted CE as it yields higher global IoU and, more importantly, an IoU gain among the smaller traffic signs with no additional computational resources. [ABSTRACT FROM AUTHOR]

In the manuscript, the issue of detecting and segmenting out pavement defects on highway roads is addressed. Specifically, computer vision (CV) methods are developed and applied to the problem based on deep learning of convolutional neural networks (ConvNets). A novel neural network structure is considered, based on a pipeline of three ConvNets and endowed with the capacity for context awareness, which improves grid-based search for defects on orthoframes by considering the surrounding image content—an approach, which essentially draws inspiration from how humans tend to solve the task of image segmentation. Also, methods for assessing the quality of segmentation are discussed. The contribution also describes the complete procedure of working with pavement defects in an industrial setting, involving the workcycle of defect annotation, ConvNet training and validation. The results of ConvNet evaluation provided in the paper hint at a successful implementation of the proposed technique. [ABSTRACT FROM AUTHOR]