Deep learning for crack-like object detection / Kaige Zhang, Postdoctoral Research Associate, Department of Computer Science, University of Minnesota--Twin Cities, St Paul, MN, USA ; Heng-Da Cheng, Full Professor, Department of Computer Science, Adjunct Full Professor, Department of Electrical Engineering, Utah State University, Logan, UT, USA.
| Author/creator | Zhang, Kaige, 1987- |
| Other author | Cheng, Heng-Da. |
| Format | Electronic |
| Edition | First edition. |
| Publication Info | Boca Raton : CRC Press, Taylor & Francis Grooup, 2023. |
| Description | 100 pages : illustrations ; 22 cm |
| Supplemental Content | Full text available from Taylor & Francis eBooks |
| Supplemental Content | Full text available from eBooks on EBSCOhost |
| Subjects |
| Abstract | "With the development of artificial intelligence (AI), the deep learning technique has achieved great success. However, using deep learning for high accurate crack localization is non-trivial. Based on deep learning, this book has solved a bunch of important issues existing in crack-like object detection, and finished a practical smart pavement surface inspection system. By introducing those method and the system, this book gives the reader an easy way to get into the computer vision and deep learning research area. In addition, this research performs a preliminary study about the future AI system, which provides a concept that has potential to realize fully automatic crack detection without human's intervention"-- Provided by publisher. |
| General note | "A Science Publishers book." |
| Bibliography note | Includes bibliographical references and index. |
| Access restriction | Available only to authorized users. |
| Technical details | Mode of access: World Wide Web |
| Genre/form | Electronic books. |
| LCCN | 2022039179 |
| ISBN | 9781032181189 (hbk) |
| ISBN | 9781032181196 (pbk) |
| ISBN | (ebk) |
Availability
| Library | Location | Call Number | Status | Item Actions |
|---|---|---|---|---|
| Electronic Resources | ✔ Available |