论文标题
部署过程中对象检测性能的在线监视
Online Monitoring of Object Detection Performance During Deployment
论文作者
论文摘要
在部署期间，预计对象检测器将以其测试数据集报告的类似性能级别运行。但是，当部署在不同且复杂的环境条件下运行的板载移动机器人时，检测器的性能会波动，偶尔不会在没有警告的情况下降低。未被发现，这可能会导致机器人根据低质量和不可靠的对象检测采取不安全和冒险的动作。我们解决了这个问题，并引入了一个级联的神经网络，该网络通过在输入帧的滑动窗口上预测其平均平均精度（MAP）的质量来监视对象检测器的性能。拟议的级联网络利用对象检测器的深神经网络的内部特征。我们使用自主驾驶数据集和对象探测器的不同组合评估我们提出的方法。
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.
