Performance monitoring of object detection is crucial for safety-critical applications such as autonomous vehicles that operate under varying and complex environmental conditions. Currently, object detectors are evaluated using summary metrics based on a single dataset that is assumed to be representative of all future deployment conditions. In practice, this assumption does not hold, and the performance fluctuates as a function of the deployment conditions. To address this issue, we propose an introspection approach to performance monitoring during deployment without the need for ground truth data. We do so by predicting when the per-frame mean average precision drops below a critical threshold using the detector's internal features. We quantitatively evaluate and demonstrate our method's ability to reduce risk by trading off making an incorrect decision by raising the alarm and absenting from detection.
Cumulative Consensus Score: Label-Free and Model-Agnostic Evaluation of Object Detectors in Deployment
DLR Institute of Systems Engineering for Future Mobility（德意志航天中心未来交通系统工程研究所）；Carl von Ossietzky University of Oldenburg（奥尔登堡卡尔·冯·奥西特齐大学）；Chalmers University of Technology（查尔姆斯理工大学）
Global Offshore Wind Infrastructure: Deployment and Operational Dynamics from Dense Sentinel-1 Time Series
Earth Observation Center (EOC), German Aerospace Center (DLR)（地球观测中心（EOC），德国航空航天中心（DLR））；Institute for Geography and Geology, University of Wuerzburg（地理与地质研究所，乌尔姆大学）
