Deep learning based tools for drone surveillance and detection in adverse environmental conditions
This study explores the design of an automated Machine pipeline comprising state-of-theart image enhancement and object detection algorithms as an aid for air traffic controllers to quickly spot and identify drone incursions in the surrounding airspace. Experiments were conducted to evaluate the d...
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| Other Authors: | |
| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/159074 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | This study explores the design of an automated Machine pipeline comprising state-of-theart image enhancement and object detection algorithms as an aid for air traffic controllers to
quickly spot and identify drone incursions in the surrounding airspace. Experiments were
conducted to evaluate the drone detection performance of the Machine pipeline by itself, of
human capabilities by themselves and a Human-Machine collaboration with human
operators and the Machine pipeline.
Results suggest that by human effort alone, drone detectable range and spatial awareness
were lacking for effective detection. By machine effort alone, presence of errors limits use
of the Machine pipeline in actual air traffic management where there is a low tolerance for
errors for safety reasons. Rather, a Human-Machine collaboration is shown to be optimal as
the Human and Machine components compensate for each other’s shortcomings while
complementing in strong points, leading to improved drone detection performance. |
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