I am developing an iOS app that uses YOLOv8 for object detection and aims to detect objects at 60 FPS using the UltraWide camera. My goal is to process every frame within captureOutput and utilize the detected data (such as coordinates) for each one. I have a question regarding how background thread processing behaves in this scenario. Does the size of the YOLO model (n, s, m, etc.) or the weight of the operations inside captureOutput affect the number of frames that can be successfully processed? Specifically, I would like to know if all frames will be processed sequentially with a delay due to heavy processing in the background, or if some frames will be dropped and not processed at all. Any insights on how to handle this would be greatly appreciated. Thank you!

I’m working on real-time object detection using YOLOv8, but I only need to detect objects in approximately 40% of the screen area. Is it possible to limit the captureOut method to focus solely on that specific region of the screen? If this isn’t feasible, I’m considering an approach where the full-screen pixel buffer is captured and then cropped to the target area before running detection. However, I’m concerned about how this might affect real-time performance. I’d appreciate any insights on how to maintain real-time performance or suggestions for better alternatives. Thank you!