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

• URL: http://arxiv.org/abs/2506.21486v1
• Date: Thu, 26 Jun 2025 17:14:37 GMT
• Title: Towards Reliable Detection of Empty Space: Conditional Marked Point Processes for Object Detection
• Authors: Tobias J. Riedlinger, Kira Maag, Hanno Gottschalk,
• Abstract summary: Deep neural networks have set the state-of-the-art in computer vision tasks such as bounding box detection and semantic segmentation.<n> Object detectors and segmentation models assign confidence scores to predictions, reflecting the model's uncertainty in object detection or pixel-wise classification.<n>We propose an object detection model grounded in spatial statistics.
• Score: 1.693200946453174
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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