River Ice Segmentation with Deep Learning

Abstract

This paper deals with the problem of computing surface ice concentration for two different types of ice from digital images of river surface. It presents the results of attempting to solve this problem using several state of the art semantic segmentation methods based on deep convolutional neural networks (CNNs). This task presents two main challenges - very limited availability of labeled training data and presence of noisy labels due to the great difficulty of visually distinguishing between the two types of ice, even for human experts. The results are used to analyze the extent to which some of the best deep learning methods currently in existence can handle these challenges. The code and data used in the experiments are made publicly available to facilitate further work in this domain.

supplementary: http://webdocs.cs.ualberta.ca/~vis/asingh1/docs/river_ice_segm_supp.pdf

References in corpus (7)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
• Neural Architecture Search with Reinforcement Learning
• DeepPap: Deep Convolutional Networks for Cervical Cell Classification
• FusionSeg: Learning to combine motion and appearance for fully automatic segmention of generic objects in videos
• River Ice Segmentation with Deep Learning
• Deep Learning of Cell Classification using Microscope Images of Intracellular Microtubule Networks

Cited by in corpus (2)

• River Ice Segmentation with Deep Learning
• UVid-Net: Enhanced Semantic Segmentation of UAV Aerial Videos by Embedding Temporal Information