Uncertainty for Identifying Open-Set Errors in Visual Object Detection

• URL: http://arxiv.org/abs/2104.01328v1
• Date: Sat, 3 Apr 2021 07:12:31 GMT
• Title: Uncertainty for Identifying Open-Set Errors in Visual Object Detection
• Authors: Dimity Miller, Niko S\"underhauf, Michael Milford and Feras Dayoub
• Abstract summary: GMM-Det is a real-time method for extracting uncertainty from object detectors to identify and reject open-set errors. We show that GMM-Det consistently outperforms existing uncertainty techniques for identifying and rejecting open-set detections.
• Score: 31.533136658421892
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deployed into an open world, object detectors are prone to a type of false positive detection termed open-set errors. We propose GMM-Det, a real-time method for extracting epistemic uncertainty from object detectors to identify and reject open-set errors. GMM-Det trains the detector to produce a structured logit space that is modelled with class-specific Gaussian Mixture Models. At test time, open-set errors are identified by their low log-probability under all Gaussian Mixture Models. We test two common detector architectures, Faster R-CNN and RetinaNet, across three varied datasets spanning robotics and computer vision. Our results show that GMM-Det consistently outperforms existing uncertainty techniques for identifying and rejecting open-set detections, especially at the low-error-rate operating point required for safety-critical applications. GMM-Det maintains object detection performance, and introduces only minimal computational overhead. We also introduce a methodology for converting existing object detection datasets into specific open-set datasets to consistently evaluate open-set performance in object detection. Code for GMM-Det and the dataset methodology will be made publicly available.

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