In object detection, false negatives arise when a detector fails to detect a target object. To understand why object detectors produce false negatives, we identify five 'false negative mechanisms', where each mechanism describes how a specific component inside the detector architecture failed. Focusing on two-stage and one-stage anchor-box object detector architectures, we introduce a framework for quantifying these false negative mechanisms. Using this framework, we investigate why Faster R-CNN and RetinaNet fail to detect objects in benchmark vision datasets and robotics datasets. We show that a detector's false negative mechanisms differ significantly between computer vision benchmark datasets and robotics deployment scenarios. This has implications for the translation of object detectors developed for benchmark datasets to robotics applications. Code is publicly available at https://github.com/csiro-robotics/fn_mechanisms

翻译：在物体探测中,当探测器无法检测目标对象时,会出现假阴差。为了理解为什么天体探测器产生假阴差,我们确定了五个“假阴差机制 ”, 其中每个机制都描述了探测器结构中的具体部件是如何失灵的。 聚焦于两个阶段和一个级锚框天体探测器结构, 我们引入了一个框架来量化这些假阴差机制。 使用这个框架, 我们调查为什么更快的 R- CNN 和Retinnet 无法在基准视觉数据集和机器人数据集中探测对象。 我们显示, 探测器的假阴差机制在计算机视觉基准数据集和机器人部署设想之间有很大差异。 这对为机器人应用基准数据集开发的天体探测器的翻译有影响。 代码可在 https://github.com/csiro-robotics/fn_mechanisms上公开查阅。