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During deployment, an object detector is expected to operate at a similarperformance level reported on its testing dataset. However, when deployedonboard mobile robots that operate under varying and complex environmentalconditions, the detector's performance can fluctuate and occasionally degradeseverely without warning. Undetected, this can lead the robot to take unsafeand risky actions based on low-quality and unreliable object detections. Weaddress this problem and introduce a cascaded neural network that monitors theperformance of the object detector by predicting the quality of its meanaverage precision (mAP) on a sliding window of the input frames. The proposedcascaded network exploits the internal features from the deep neural network ofthe object detector. We evaluate our proposed approach using differentcombinations of autonomous driving datasets and object detectors.

Online Monitoring of Object Detection Performance During Deployment