Open AccessComparison of Semantic Convolution Neural Networks on the Example of Crack Segmentation in Asphalt Images
Author(s) -
Светлана Мустафина,
Andrey Akimov,
Светлана Мустафина,
A.S. Plotnikova
Publication year - 2021
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.20.3.2288
Subject(s) - segmentation , convolutional neural network , computer science , artificial intelligence , convolution (computer science) , process (computing) , artificial neural network , road surface , pattern recognition (psychology) , computer vision , machine learning , engineering , operating system , civil engineering
The article is devoted to a comparative analysis of the effectiveness of convolutional neural networks for semantic segmentation of road surface damage marking. Currently, photo and video surveillance methods are used to control the condition of the road surface. Assessing and analyzing new manual data can take too long. Thus, a completely different procedure is required to inspect and assess the state of controlled objects using technical vision. The authors compared 3 neural networks (Unet, Linknet, PSPNet) used in semantic segmentation using the example of the Crack500 dataset. The proposed architectures have been implemented in the Keras and TensorFlow frameworks. The compared models of neural convolutional networks effectively solve the assigned tasks even with a limited amount of training data. High accuracy is observed. The considered models can be used in various segmentation tasks. The results obtained can be used in the process of modeling, monitoring, and predicting the wear of the road surface.