Lane detection and tracking control for unmanned aerial vehicles using image segmentation
Lane detection has been widely used in land-based vehicles to carry out autonomous driving. The same concept can be applied to aerial vehicles to enable autonomous maneuvering based on the detected runway. This opens the possibilities of automated capabilities such as path tracking, automatic landin...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/157481 |
| _version_ | 1826128528098197504 |
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| author | Tan, Zhi Jie |
| author2 | Lyu Chen |
| author_facet | Lyu Chen Tan, Zhi Jie |
| author_sort | Tan, Zhi Jie |
| collection | NTU |
| description | Lane detection has been widely used in land-based vehicles to carry out autonomous driving. The same concept can be applied to aerial vehicles to enable autonomous maneuvering based on the detected runway. This opens the possibilities of automated capabilities such as path tracking, automatic landing, and automatic take-off.
However, the integration of real-time lane detection on an UAV is still an unconventional topic. Thus, this project aims to develop a robust runway detection system through the combination of image segmentation and computer vision techniques. The Convolutional Neural Network architectures used were E-Net and U-Net. Training data were self-generated and labelled using MATLAB image segmenter.
The optimized model weights were integrated with the DJI Tello drone using the DJITelloPy Application Programming Interface (API). Real-time detection was carried out to fulfill the autonomous actions such as path tracking, automatic landing, and automatic takeoff. This final year project serves as a study to future research on the integration of autonomous navigation and UAVs. |
| first_indexed | 2024-10-01T07:26:24Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/157481 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:26:24Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1574812023-03-04T20:17:13Z Lane detection and tracking control for unmanned aerial vehicles using image segmentation Tan, Zhi Jie Lyu Chen School of Mechanical and Aerospace Engineering lyuchen@ntu.edu.sg Engineering::Aeronautical engineering Lane detection has been widely used in land-based vehicles to carry out autonomous driving. The same concept can be applied to aerial vehicles to enable autonomous maneuvering based on the detected runway. This opens the possibilities of automated capabilities such as path tracking, automatic landing, and automatic take-off. However, the integration of real-time lane detection on an UAV is still an unconventional topic. Thus, this project aims to develop a robust runway detection system through the combination of image segmentation and computer vision techniques. The Convolutional Neural Network architectures used were E-Net and U-Net. Training data were self-generated and labelled using MATLAB image segmenter. The optimized model weights were integrated with the DJI Tello drone using the DJITelloPy Application Programming Interface (API). Real-time detection was carried out to fulfill the autonomous actions such as path tracking, automatic landing, and automatic takeoff. This final year project serves as a study to future research on the integration of autonomous navigation and UAVs. Bachelor of Engineering (Aerospace Engineering) 2022-05-18T07:28:50Z 2022-05-18T07:28:50Z 2022 Final Year Project (FYP) Tan, Z. J. (2022). Lane detection and tracking control for unmanned aerial vehicles using image segmentation. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157481 https://hdl.handle.net/10356/157481 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Aeronautical engineering Tan, Zhi Jie Lane detection and tracking control for unmanned aerial vehicles using image segmentation |
| title | Lane detection and tracking control for unmanned aerial vehicles using image segmentation |
| title_full | Lane detection and tracking control for unmanned aerial vehicles using image segmentation |
| title_fullStr | Lane detection and tracking control for unmanned aerial vehicles using image segmentation |
| title_full_unstemmed | Lane detection and tracking control for unmanned aerial vehicles using image segmentation |
| title_short | Lane detection and tracking control for unmanned aerial vehicles using image segmentation |
| title_sort | lane detection and tracking control for unmanned aerial vehicles using image segmentation |
| topic | Engineering::Aeronautical engineering |
| url | https://hdl.handle.net/10356/157481 |
| work_keys_str_mv | AT tanzhijie lanedetectionandtrackingcontrolforunmannedaerialvehiclesusingimagesegmentation |