Object detection model pruning with application to human recognition in UAV footage
| dc.contributor.advisor | Кузьменко, Дмитро | uk_UA |
| dc.contributor.author | Безбородов, Владислав | uk_UA |
| dc.date.accessioned | 2025-09-04T13:10:25Z | |
| dc.date.available | 2025-09-04T13:10:25Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | This thesis explores the application of the model optimization techniques in object detection field with a focus on human recognition from UAV footage. Limitations of resource constrained devices and deployment of accurate yet lightweight models is a challenging task. To address this, we examine three core optimization approaches: quantization, pruning, and knowledge distillation. Each method is investigated and applied in the context of YOLOv8-based detectors. Through experimental evaluation and comparative analysis with models trained from scratch, we demonstrate these techniques can significantly reduce model size and inference latency while preserving favorable performance. | en_US |
| dc.identifier.uri | https://ekmair.ukma.edu.ua/handle/123456789/36444 | |
| dc.language.iso | en_US | en_US |
| dc.status | first published | en_US |
| dc.subject | model optimization | en_US |
| dc.subject | object detection | en_US |
| dc.subject | UAV footage | en_US |
| dc.subject | constrained devices | en_US |
| dc.subject | bachelor`s thesis | en_US |
| dc.title | Object detection model pruning with application to human recognition in UAV footage | en_US |
| dc.type | Other | en_US |
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