Uncertainty Aware Proposal Segmentation for Unknown Object Detection
Uncertainty Aware Proposal Segmentation for Unknown Object Detection
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DOI：
10.1109/wacvw54805.2022.00030
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Yimeng Li;J. Kosecka
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文献类型：
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作者：
Yimeng Li;J. Kosecka
最近在自动驾驶等现实世界应用中部署深度神经网络进行对象检测的努力假设在训练期间已观察到所有相关的对象类别。当测试数据未在训练集中表示时，量化这些模型在设置中的性能主要集中于为语义分割训练的模型的像素级不确定性估计技术。本文提出利用语义分割模型的额外预测并量化其置信度，然后将对象假设分类为已知与未知、分布对象。我们使用区域提议网络（RPN）生成的对象提议，并使用径向基函数网络（RBFN）调整语义分割的距离感知不确定性估计，以进行与类别无关的对象掩模预测。然后使用增强的对象建议来训练已知与未知对象类别的分类器。实验结果表明，所提出的方法实现了与最先进的未知物体检测方法并行的性能，并且还可以有效地用于降低物体检测器的误报率。我们的方法非常适合通过语义分割获得的非对象背景类别的预测可靠的应用。
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.