Uncertainty Aware Proposal Segmentation for Unknown Object Detection
This addresses a critical safety issue in autonomous driving by enabling detection of out-of-distribution objects, though it is incremental as it builds on existing segmentation and proposal techniques.
The paper tackles the problem of detecting unknown objects not seen during training in real-world applications like autonomous driving, by using uncertainty-aware semantic segmentation and object proposals to classify known vs. unknown objects, achieving parallel performance to state-of-the-art methods and reducing false positive rates.
Recent efforts in deploying Deep Neural Networks for object detection in real world applications, such as autonomous driving, assume that all relevant object classes have been observed during training. Quantifying the performance of these models in settings when the test data is not represented in the training set has mostly focused on pixel-level uncertainty estimation techniques of models trained for semantic segmentation. This paper proposes to exploit additional predictions of semantic segmentation models and quantifying its confidences, followed by classification of object hypotheses as known vs. unknown, out of distribution objects. We use object proposals generated by Region Proposal Network (RPN) and adapt distance aware uncertainty estimation of semantic segmentation using Radial Basis Functions Networks (RBFN) for class agnostic object mask prediction. The augmented object proposals are then used to train a classifier for known vs. unknown objects categories. Experimental results demonstrate that the proposed method achieves parallel performance to state of the art methods for unknown object detection and can also be used effectively for reducing object detectors' false positive rate. Our method is well suited for applications where prediction of non-object background categories obtained by semantic segmentation is reliable.