论文标题
黑匣子里有什么？对象检测器内的假负机制
What's in the Black Box? The False Negative Mechanisms Inside Object Detectors
论文作者
论文摘要
在对象检测中，当检测器未能检测到目标对象时，会出现假阴性。为了了解为什么对象检测产生虚假负面因素，我们确定了五种“假负机制”，其中每个机制都描述了检测器体系结构内部的特定组件如何失败。着眼于两阶段和单阶段的锚点对象检测器体系结构，我们引入了一个框架来量化这些虚假的负面机制。使用此框架，我们调查了为什么更快的R-CNN和视网膜无法检测基准视觉数据集和机器人数据集中的对象。我们表明，检测器的假负机制在计算机视觉基准数据集和机器人部署方案之间存在显着差异。这对基准数据集开发为机器人应用程序的对象检测器的翻译具有影响。代码可在https://github.com/csiro-robotics/fn_mechanisms上公开获取
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
