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.