Yu, XiaohuiDehghani Firoozabadi, Davood2025-11-112025-11-112025-07-282025-11-11https://hdl.handle.net/10315/43264High-accuracy object detection on resource-constrained devices is essential for many applications including autonomous systems, agriculture, and mobile computing. However, deploying high-performance object detection models on these devices is impractical due to computational limitations, and transmitting and processing all data on a much more powerful remote server running significantly more complex and accurate models, known as full cloud offloading, incurs high latency and cost. This thesis proposes a selective cloud offloading framework that balances prediction accuracy and processing cost. A lightweight edge model makes initial predictions using conformal prediction to quantify uncertainty. Only high-uncertainty regions are offloaded to the cloud for refinement by more powerful models. To further optimize efficiency, multiple uncertain regions are merged into a single image before offloading, reducing transmission and processing costs. The system is evaluated on real datasets, demonstrating substantial accuracy improvements with minimal additional overhead.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Computer scienceInformation technologyArtificial intelligenceElectronic Thesis or Dissertation2025-11-11Conformal predictionObject detectionEdge computingCloud offloading