High-accuracy object detection on resource-constrained devices is becoming increasingly important for applications in autonomous systems, smart surveillance, 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 is constrained by network bandwidth. In this paper, we propose a selective cloud offloading framework that provides users with control over the tradeoff between prediction accuracy and processing cost. Our approach employs a lightweight object detection model on the edge to make initial predictions, leveraging conformal prediction to quantify uncertainty. Only high-uncertainty regions are offloaded to the cloud, where more powerful models refine predictions, improving overall detection accuracy. To further optimize efficiency, multiple uncertain regions are combined into a single image before offloading, reducing transmission and processing costs. We present the architecture of our system and evaluate its performance on real datasets, demonstrating that it achieves cloud-level accuracy while significantly reducing offloading overhead.