利用空间关系图修复第一人称视角下的物体检测错误
Enhancing Egocentric Object Detection in Static Environments using Graph-based Spatial Anomaly Detection and Correction
- 构建图神经网络，基于物体邻近关系识别异常检测结果
- 在静态场景中使mAP@50提升最高达4%
- 适合需要高可靠性的智能眼镜、自动驾驶等场景
在许多静态环境应用中，物体的空间布局具有高度一致性。然而，当前主流目标检测模型往往忽略这一空间先验，导致在杂乱或遮挡场景中出现预测不一致、漏检或误分类。本文提出一种基于图的后处理流程，显式建模物体间的空间关系，以修正第一人称视角图像中的检测异常。通过在人工标注数据上训练的图神经网络（GNN），模型可识别无效的物体类别标签，并根据其邻域上下文预测修正后的类别。我们在YOLOv7和RT-DETR等标准检测器上验证了该方法作为独立异常检测与修正框架及后处理模块的有效性。实验表明，引入空间推理显著提升了检测性能，最高实现mAP@50提升4%。该方法展示了利用环境空间结构增强检测系统可靠性的潜力。
原文摘要 · Abstract (English)
In many real-world applications involving static environments, the spatial layout of objects remains consistent across instances. However, state-of-the-art object detection models often fail to leverage this spatial prior, resulting in inconsistent predictions, missed detections, or misclassifications, particularly in cluttered or occluded scenes. In this work, we propose a graph-based post-processing pipeline that explicitly models the spatial relationships between objects to correct detection anomalies in egocentric frames. Using a graph neural network (GNN) trained on manually annotated data, our model identifies invalid object class labels and predicts corrected class labels based on their neighbourhood context. We evaluate our approach both as a standalone anomaly detection and correction framework and as a post-processing module for standard object detectors such as YOLOv7 and RT-DETR. Experiments demonstrate that incorporating this spatial reasoning significantly improves detection performance, with mAP@50 gains of up to 4%. This method highlights the potential of leveraging the environment's spatial structure to improve reliability in object detection systems.
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