标题： 面向可靠的空闲空间检测：对象检测的条件标记点过程 显示英文标题

标题： Towards Reliable Detection of Empty Space: Conditional Marked Point Processes for Object Detection

摘要： 深度神经网络在计算机视觉任务中，如边界框检测和语义分割，已经达到了最先进的水平。 目标检测器和分割模型会为预测分配置信度分数，反映模型在目标检测或像素级分类中的不确定性。 然而，这些置信度估计常常是不准确的，因为它们的架构和损失函数是针对任务性能进行优化的，而不是基于概率基础。 即使预测是校准良好的，目标检测器也无法量化检测到的边界框之外的不确定性，即模型不会对没有检测到目标的区域是否真正没有障碍物进行概率评估。 这在自动驾驶等应用中构成了安全风险，因为在空区域中的不确定性仍未被探索。 在本工作中，我们提出了一种基于空间统计的目标检测模型。 边界框数据匹配标记点过程的实现，该过程常用于描述作为边界框中心识别的空间点事件的概率发生，其中标记用于描述边界框的空间扩展和类别。 我们的统计框架实现了基于似然的训练，并提供了关于某个区域是否可行驶（即无物体）的明确置信度估计。 我们通过校准评估和性能评估来证明了我们方法的有效性。 显示英文摘要

摘要： Deep neural networks have set the state-of-the-art in computer vision tasks such as bounding box detection and semantic segmentation. Object detectors and segmentation models assign confidence scores to predictions, reflecting the model's uncertainty in object detection or pixel-wise classification. However, these confidence estimates are often miscalibrated, as their architectures and loss functions are tailored to task performance rather than probabilistic foundation. Even with well calibrated predictions, object detectors fail to quantify uncertainty outside detected bounding boxes, i.e., the model does not make a probability assessment of whether an area without detected objects is truly free of obstacles. This poses a safety risk in applications such as automated driving, where uncertainty in empty areas remains unexplored. In this work, we propose an object detection model grounded in spatial statistics. Bounding box data matches realizations of a marked point process, commonly used to describe the probabilistic occurrence of spatial point events identified as bounding box centers, where marks are used to describe the spatial extension of bounding boxes and classes. Our statistical framework enables a likelihood-based training and provides well-defined confidence estimates for whether a region is drivable, i.e., free of objects. We demonstrate the effectiveness of our method through calibration assessments and evaluation of performance.