部署过程中目标检测性能的在线监控
Online Monitoring of Object Detection Performance During Deployment
摘要：
在部署过程中，对象检测器预计将在其测试数据集上报告的类似性能水平下运行。然而，当部署在变化和复杂的环境条件下运行的车载移动机器人时，探测器的性能可能会波动，偶尔会在没有警告的情况下严重下降。未被检测到，这可能导致机器人基于低质量和不可靠的目标检测采取不安全和危险的行动。我们解决这个问题，并介绍了一个级联的神经网络，监测对象检测器的性能，通过预测其平均精度（mAP）的输入帧的滑动窗口上的质量。所提出的级联网络利用来自对象检测器的深度神经网络的内部特征。我们使用自动驾驶数据集和对象检测器的不同组合来评估我们提出的方法。
英文摘要：
During deployment, an object detector is expected to operate at a similar performance level reported on its testing dataset. However, when deployed onboard mobile robots that operate under varying and complex environmental conditions, the detector's performance can fluctuate and occasionally degrade severely without warning. Undetected, this can lead the robot to take unsafe and risky actions based on low-quality and unreliable object detections. We address this problem and introduce a cascaded neural network that monitors the performance of the object detector by predicting the quality of its mean average precision (mAP) on a sliding window of the input frames. The proposed cascaded network exploits the internal features from the deep neural network of the object detector. We evaluate our proposed approach using different combinations of autonomous driving datasets and object detectors.
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