Kaige Zhang, Heng-Da Cheng (Utha State Uni.)

Deep Learning for Crack-Like Object Detection

• Gebundenes Buch

Produktbeschreibung

Accurately detecting crack localization is not an easy task. This book addresses important issues in detecting crack-like objects and provides a practical smart pavement surface inspection system using deep learning.

Produktdetails

• Verlag: Taylor & Francis Ltd
• Seitenzahl: 100
• Erscheinungstermin: 20. März 2023
• Englisch
• Abmessung: 223mm x 144mm x 12mm
• Gewicht: 228g
• ISBN-13: 9781032181189
• ISBN-10: 1032181184
• Artikelnr.: 67258961

Autorenporträt

Kaige Zhang has a B.S. degree (2011) in electronic engineering from the Harbin Institute of Technology, China, and a Ph.D. degree (2019) in computer science from Utah State University, USA. His research interests include computer vision, machine learning, and the applications on intelligent transportation systems, precision agriculture, and biomedical data analytics. Dr. Zhang has been the reviewer for many top journals in his research areas, such as IEEE Transactions on ITS, IEEE Trans. On T-IV, J. of Comput. in Civil Eng., Scientific Report, etc. Heng-Da Cheng has a Ph.D. in Electrical Engineering from Purdue University, West Lafayette, IN, USA in 1985 under the supervision Prof. K. S. Fu. He is a Full Professor with the Department of Computer Science, Utah State University, Logan, UT. He has authored over 350 technical papers and is the Associate Editor of Pattern Recognition, Information Sciences, and New Mathematics and Natural Computation.

Inhaltsangabe

Introduction. Crack Detection with Deep Classification Network. Crack
Detection with Fully Convolutional Network. Crack Detection with Generative
Adversarial Learning. Self-Supervised Structure Learning for Crack
Detection. Deep Edge Computing. Conclusion and Discussion.