Per‑Object IoU Forecasting for Deadline‑Aware Real‑Time Embedded Detection Control

Real-time object detection on edge platforms is constrainedby inference latency due to limited resources. Existing runtime controllersadapt detector configurations using coarse frame-level feedback, but failto account for object-level accuracy degradation caused by detection tem-poral mismatch. This leads to suboptimal scheduling decisions under dy-namic workloads. We propose a lightweight per-object IoU decay modelthat predicts accuracy loss and derives closed-form deadline estimatesfor each detected object. These deadline predictions provide fine-grained,deadline-aware feedback that can be integrated into runtime control poli-cies. We integrate the model into a new closed-loop detector selectionframework and evaluate it under streaming conditions on Argoverse-HDand MOT17 across heterogeneous hardware platforms. Experiments onreal hardware testbeds show that our model and control algorithm im-prove deadline prediction accuracy by 56% and streaming AP by 39% onaverage across 71 video scenarios, while incurring acceptable overhead.The code is available at https://github.com/3Eerfan/OLAP-ECCV2026.