论文标题
对象检测中的错误检测（阳性和负面）
False Detection (Positives and Negatives) in Object Detection
论文作者
论文摘要
对象检测是视觉感知系统的非常重要的功能。自从基于现代深度学习的检测器基于猪的经典对象检测早期以来，对象检测的准确性有所提高。两个阶段探测器通常比单阶段的探测器更高。两种类型的检测器都使用图像矩形区域的搜索空间的某种形式的量化。量化的元素远远超过真实对象。这些边界框被过滤的方式可能导致虚假的正面和假阴性。这项经验实验研究探讨了用标记数据降低假阳性和负面因素的方法。在此过程中，在2019年OpenImage 2019对象检测数据集中也发现了标签不足。
Object detection is a very important function of visual perception systems. Since the early days of classical object detection based on HOG to modern deep learning based detectors, object detection has improved in accuracy. Two stage detectors usually have higher accuracy than single stage ones. Both types of detectors use some form of quantization of the search space of rectangular regions of image. There are far more of the quantized elements than true objects. The way these bounding boxes are filtered out possibly results in the false positive and false negatives. This empirical experimental study explores ways of reducing false positives and negatives with labelled data.. In the process also discovered insufficient labelling in Openimage 2019 Object Detection dataset.
