Yang Liu

TL;DR: The CrackNet, an efficient architecture based on the Convolutional Neural Network, is proposed in this article for automated pavement crack detection on 3D asphalt surfaces with explicit objective of pixel‐perfect accuracy.

TL;DR: A new recurrent unit, gated recurrent multilayer perceptron (GRMLP), is proposed to recursively update the internal memory of CrackNet‐R, a recurrent neural network for fully automated pixel‐level crack detection on three‐dimensional asphalt pavement surfaces.

TL;DR: It is shown that CrackNet-V yields better overall performance particularly in detecting fine cracks compared with CrackNet, and further reveals the advantages of deep learning techniques for automated pixel-level pavement crack detection.

TL;DR: CrackNet is the result of an 18-month collaboration within a 10-person team to develop a deep learning–based pavement crack detection software that demonstrated successes in terms of accuracy, efficiency, and efficiency.

TL;DR: This data shows that pavement engineers define various distress categories based on pavement types and image-based systems are becoming popular to collect pavement condition data for pavement management activities.