A vision system for detection of construction materials
With Deep Learning (DL) emerging from Machine Learning (ML) to become one of the greatest technological advancement and invention in today’s day and age. DL methods and techniques are becoming a pivotal part in our initiatives to Industry 4.0. Convolutional Neural Network (CNN) is an important archi...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/139195 |
| _version_ | 1826125555234242560 |
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| author | Kabilan Elangovan |
| author2 | CHEAH Chien Chern |
| author_facet | CHEAH Chien Chern Kabilan Elangovan |
| author_sort | Kabilan Elangovan |
| collection | NTU |
| description | With Deep Learning (DL) emerging from Machine Learning (ML) to become one of the greatest technological advancement and invention in today’s day and age. DL methods and techniques are becoming a pivotal part in our initiatives to Industry 4.0. Convolutional Neural Network (CNN) is an important architecture of DL. CNN has achieved astounding results in the area of image recognition and object detection. However, CNN can be extensive and thus carrying a high load of computational processes. As such You Only Look Once (YOLO), a form of CNN was developed to perform object detection and classification with a smaller architecture and faster computing capabilities. Therefore, the aim of this project is to employ YOLO as main object detection technique to detect concrete structures and various concrete defects as an initiative to improve the productivity in Construction Industries. Furthermore, this project also focuses on a Computer Vision (CV) technique to retrieve the third dimensional parameter of concrete structures via the use of detection results from YOLO. |
| first_indexed | 2024-10-01T06:38:14Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/139195 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:38:14Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1391952023-07-07T18:53:16Z A vision system for detection of construction materials Kabilan Elangovan CHEAH Chien Chern School of Electrical and Electronic Engineering ecccheah@ntu.edu.sg Engineering::Electrical and electronic engineering With Deep Learning (DL) emerging from Machine Learning (ML) to become one of the greatest technological advancement and invention in today’s day and age. DL methods and techniques are becoming a pivotal part in our initiatives to Industry 4.0. Convolutional Neural Network (CNN) is an important architecture of DL. CNN has achieved astounding results in the area of image recognition and object detection. However, CNN can be extensive and thus carrying a high load of computational processes. As such You Only Look Once (YOLO), a form of CNN was developed to perform object detection and classification with a smaller architecture and faster computing capabilities. Therefore, the aim of this project is to employ YOLO as main object detection technique to detect concrete structures and various concrete defects as an initiative to improve the productivity in Construction Industries. Furthermore, this project also focuses on a Computer Vision (CV) technique to retrieve the third dimensional parameter of concrete structures via the use of detection results from YOLO. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-05-18T03:18:15Z 2020-05-18T03:18:15Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/139195 en A1036-191 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Kabilan Elangovan A vision system for detection of construction materials |
| title | A vision system for detection of construction materials |
| title_full | A vision system for detection of construction materials |
| title_fullStr | A vision system for detection of construction materials |
| title_full_unstemmed | A vision system for detection of construction materials |
| title_short | A vision system for detection of construction materials |
| title_sort | vision system for detection of construction materials |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/139195 |
| work_keys_str_mv | AT kabilanelangovan avisionsystemfordetectionofconstructionmaterials AT kabilanelangovan visionsystemfordetectionofconstructionmaterials |