论文标题
自主检查机器人的自我校准异常和更改检测
Self-Calibrating Anomaly and Change Detection for Autonomous Inspection Robots
论文作者
论文摘要
在过去的几十年中，自动检测视觉异常和环境变化一直是机器学习和计算机视觉领域的重复关注的话题。视觉异常或更改检测算法标识了与参考图像或数据集不同的图像区域。现有的大多数方法都集中在特定类别的图像或环境中的异常检测上，而通用视觉异常检测算法在文献中更稀缺。在本文中，我们提出了一个综合的深度学习框架，用于在收集参考数据集后，不需要重新培训模型后检测先验未知环境的异常和变化。我们使用SuperPoint和Superglue特征提取和匹配方法来检测基于从相似位置拍摄的参考图像以及视野部分重叠的参考图像。我们还为提出的模型引入了一种自我校准方法，以解决具有匹配阈值和环境条件的敏感性问题。为了评估所提出的框架，我们使用了一个地面机器人系统，目的是参考和查询数据收集。我们表明，可以使用所提出的方法获得高精度。我们还表明，校准过程增强了变化和异物检测性能
Automatic detection of visual anomalies and changes in the environment has been a topic of recurrent attention in the fields of machine learning and computer vision over the past decades. A visual anomaly or change detection algorithm identifies regions of an image that differ from a reference image or dataset. The majority of existing approaches focus on anomaly or fault detection in a specific class of images or environments, while general purpose visual anomaly detection algorithms are more scarce in the literature. In this paper, we propose a comprehensive deep learning framework for detecting anomalies and changes in a priori unknown environments after a reference dataset is gathered, and without need for retraining the model. We use the SuperPoint and SuperGlue feature extraction and matching methods to detect anomalies based on reference images taken from a similar location and with partial overlapping of the field of view. We also introduce a self-calibrating method for the proposed model in order to address the problem of sensitivity to feature matching thresholds and environmental conditions. To evaluate the proposed framework, we have used a ground robot system for the purpose of reference and query data collection. We show that high accuracy can be obtained using the proposed method. We also show that the calibration process enhances changes and foreign object detection performance
