论文标题
部署过程中对象检测的连续性能监视的人均地图预测
Per-frame mAP Prediction for Continuous Performance Monitoring of Object Detection During Deployment
论文作者
论文摘要
对象检测的性能监测对于在不同且复杂的环境条件下运行的自动驾驶汽车等对象检测至关重要。当前，使用基于单个数据集的摘要指标评估对象探测器，该数据集被认为代表了所有未来部署条件。实际上，此假设不存在，并且性能随部署条件的函数而波动。为了解决这个问题，我们提出了一种内省的方法来进行部署期间的性能监控方法，而无需地面真相数据。我们通过预测使用检测器的内部功能何时平均平均精度下降到关键阈值以下。我们通过发出警报和缺乏检测来进行交易，对方法进行定量评估并证明我们方法降低风险的能力。
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.
