A Deeply Supervised Convolutional Neural Network for Pavement Crack Detection With Multiscale Feature Fusion

• Published in: IEEE transaction on neural networks and learning systems Vol. 33; no. 9; pp. 4890 - 4899
• Main Authors: Qu, Zhong, Cao, Chong, Liu, Ling, Zhou, Dong-Yang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Convolution; Convolutional neural networks; Convolutional neural networks (CNNs); deep supervision; Edge detection; Explosives detection; Feature extraction; Image edge detection; Image processing; Image segmentation; Learning systems; Modules; multiscale feature fusion; Neural networks; pavement crack detection; Road maintenance; Semantics; Training

Automatic crack detection is vital for efficient and economical road maintenance. With the explosive development of convolutional neural networks (CNNs), recent crack detection methods are mostly based on CNNs. In this article, we propose a deeply supervised convolutional neural network for crack de...