  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.
