How to get pavement distress detection ready for deep learning? A systematic approach

By Markus Eisenbach, Ronny Stricker, Daniel Seichter, Karl Amende, Klaus Debes, Maximilian Sesselmann, Dirk Ebersbach, Ulrike Stoeckert, Horst-Michael Groß. IEEE International Joint Conference on Neural Network, 2017

365 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/061afb6b8d44f4fc3f336fd3c1bd657cd1c9b677

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In Civil and Structural Engineering

1. Fan Yang

Citations in the corpus

• Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection (2019, background): “Eisenbach et al [20] present a road distress dataset for training deep learning network and first evaluates and analyzes state-of-the-art approaches in pavement distress detection.”
• Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images (2018, background): “Further, although the GAPs data set (Eisenbach et al., 2017) treats the crack as a crack, in our data set, the cracks are classified into five types.”