Instance segmentation of images is an important tool for automated scene understanding. Neural networks are usually trained to optimize their overall performance in terms of accuracy. Meanwhile, in applications such as automated driving, an overlooked pedestrian seems more harmful than a falsely detected one. In this work, we present a false negative detection method for image sequences based on inconsistencies in time series of tracked instances given the availability of image sequences in online applications. As the number of instances can be greatly increased by this algorithm, we apply a false positive pruning using uncertainty estimates aggregated over instances. To this end, instance-wise metrics are constructed which characterize uncertainty and geometry of a given instance or are predicated on depth estimation. The proposed method serves as a post-processing step applicable to any neural network that can also be trained on single frames only. In our tests, we obtain an improved trade-off between false negative and false positive instances by our fused detection approach in comparison to the use of an ordinary score value provided by the instance segmentation network during inference.
翻译:图像的例分解是自动了解场景的一个重要工具。 神经网络通常经过培训, 以便在准确性方面优化其总体性能。 同时, 在自动驾驶等应用中, 被忽视的行人似乎比错误检测的行人更有害。 在这项工作中, 我们根据在线应用中图像序列的可用性, 对跟踪案例的时间序列不一致, 对图像序列提出了虚假的负面检测方法。 由于这种算法可以大大增加实例的数量, 我们使用各种实例的不确定性估计值, 使用假的正线剪裁。 为此, 构建了实例性指标, 以特定实例的不确定性和几何性为特征, 或者以深度估测为根据。 拟议方法是一个后处理步骤, 适用于任何也只能接受单一框架培训的神经网络。 在我们的测试中, 我们的导出检测方法在假负数和假正数之间实现更好的权衡。 与在推断过程中使用例分化网络提供的普通得分值相比, 我们的导式检测方法在测试中得到了更好的权衡。