论文标题
对象检测的迭代边界框注释
Iterative Bounding Box Annotation for Object Detection
论文作者
论文摘要
数字图像中用于对象检测的边界框的手动注释很繁琐，并且时间和资源消耗。在本文中，我们提出了一种半自动方法，用于有效的边界框注释。该方法将对象检测器迭代地训练在标记的图像的小批次上，并学会为下一批提出边界框，之后人类注释者只需要纠正可能的错误即可。我们提出了一个实验设置，用于模拟人类的行为并将其用于比较不同的迭代策略，例如将数据呈现给注释者的顺序。我们使用三个数据集对方法进行实验，并表明它可以大大减少人类注释的工作，从而节省了多达75％的手动注释工作。
Manual annotation of bounding boxes for object detection in digital images is tedious, and time and resource consuming. In this paper, we propose a semi-automatic method for efficient bounding box annotation. The method trains the object detector iteratively on small batches of labeled images and learns to propose bounding boxes for the next batch, after which the human annotator only needs to correct possible errors. We propose an experimental setup for simulating the human actions and use it for comparing different iteration strategies, such as the order in which the data is presented to the annotator. We experiment on our method with three datasets and show that it can reduce the human annotation effort significantly, saving up to 75% of total manual annotation work.
