Adaptation of object detection networks under anomalous conditions
Out of distribution (OOD) samples can negatively affect the performance of deep neural networks. When deep neural networks are used in cyber-physical systems, it may be vulnerable to OOD data, leading to large errors and compromise the safety of the system. This paper proposes combining OOD explanat...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/166069 |
| _version_ | 1824455697182490624 |
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| author | Koh, Rachel |
| author2 | Arvind Easwaran |
| author_facet | Arvind Easwaran Koh, Rachel |
| author_sort | Koh, Rachel |
| collection | NTU |
| description | Out of distribution (OOD) samples can negatively affect the performance of deep neural networks. When deep neural networks are used in cyber-physical systems, it may be vulnerable to OOD data, leading to large errors and compromise the safety of the system. This paper proposes combining OOD explanation and object detection model into a pipeline that maximizes efficiency and maintains accuracy across different OOD situations. We use YOLOv7 as the object detection model, which accepts different input sizes with a single set of weights. Under anomalous conditions, we can increase the input size to reduce the drop in accuracy at the expense of speed. This allows accuracy and speed to be balanced under different anomalous conditions. Alternatively, fine-tuned weights can be switched in under anomalous conditions, which shows consistent improvements though at higher costs. |
| first_indexed | 2025-02-19T03:42:19Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/166069 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:42:19Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1660692023-04-21T15:38:52Z Adaptation of object detection networks under anomalous conditions Koh, Rachel Arvind Easwaran School of Computer Science and Engineering arvinde@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Out of distribution (OOD) samples can negatively affect the performance of deep neural networks. When deep neural networks are used in cyber-physical systems, it may be vulnerable to OOD data, leading to large errors and compromise the safety of the system. This paper proposes combining OOD explanation and object detection model into a pipeline that maximizes efficiency and maintains accuracy across different OOD situations. We use YOLOv7 as the object detection model, which accepts different input sizes with a single set of weights. Under anomalous conditions, we can increase the input size to reduce the drop in accuracy at the expense of speed. This allows accuracy and speed to be balanced under different anomalous conditions. Alternatively, fine-tuned weights can be switched in under anomalous conditions, which shows consistent improvements though at higher costs. Bachelor of Engineering (Computer Science) 2023-04-19T01:15:53Z 2023-04-19T01:15:53Z 2023 Final Year Project (FYP) Koh, R. (2023). Adaptation of object detection networks under anomalous conditions. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166069 https://hdl.handle.net/10356/166069 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Koh, Rachel Adaptation of object detection networks under anomalous conditions |
| title | Adaptation of object detection networks under anomalous conditions |
| title_full | Adaptation of object detection networks under anomalous conditions |
| title_fullStr | Adaptation of object detection networks under anomalous conditions |
| title_full_unstemmed | Adaptation of object detection networks under anomalous conditions |
| title_short | Adaptation of object detection networks under anomalous conditions |
| title_sort | adaptation of object detection networks under anomalous conditions |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
| url | https://hdl.handle.net/10356/166069 |
| work_keys_str_mv | AT kohrachel adaptationofobjectdetectionnetworksunderanomalousconditions |