In object detection, the cost of labeling is much high because it needs not only to confirm the categories of multiple objects in an image but also to accurately determine the bounding boxes of each object. Thus, integrating active learning into object detection will raise pretty positive significance. In this paper, we propose a classification committee for active deep object detection method by introducing a discrepancy mechanism of multiple classifiers for samples' selection when training object detectors. The model contains a main detector and a classification committee. The main detector denotes the target object detector trained from a labeled pool composed of the selected informative images. The role of the classification committee is to select the most informative images according to their uncertainty values from the view of classification, which is expected to focus more on the discrepancy and representative of instances. Specifically, they compute the uncertainty for a specified instance within the image by measuring its discrepancy output by the committee pre-trained via the proposed Maximum Classifiers Discrepancy Group Loss (MCDGL). The most informative images are finally determined by selecting the ones with many high-uncertainty instances. Besides, to mitigate the impact of interference instances, we design a Focus on Positive Instances Loss (FPIL) to make the committee the ability to automatically focus on the representative instances as well as precisely encode their discrepancies for the same instance. Experiments are conducted on Pascal VOC and COCO datasets versus some popular object detectors. And results show that our method outperforms the state-of-the-art active learning methods, which verifies the effectiveness of the proposed method.

翻译：在目标检测中，标注成本很高，因为它不仅需要确认图像中多个目标的类别，还要精确确定每个目标的边界框。因此，将主动学习融入目标检测具有十分积极的意义。本文通过引入多分类器差异机制来选取样本以训练目标检测器，提出了一种用于主动深度目标检测的分类委员会方法。该模型包含一个主检测器和一个分类委员会。主检测器代表由已选信息图像组成的标注池训练得到的目标检测器。分类委员会的作用是从分类视角根据其不确定性值选取信息量最大的图像，这有望更关注实例的差异性和代表性。具体而言，它们通过测量由委员会（通过所提出的最大分类器差异组损失MCDGL预训练）输出的指定实例的差异来计算该实例的不确定性。最终通过选取包含大量高不确定性实例的图像来确定信息量最大的图像。此外，为了减轻干扰实例的影响，我们设计了一种专注于正实例的损失（FPIL），使委员会能够自动聚焦代表性实例并精确编码同一实例的差异。在Pascal VOC和COCO数据集上，针对一些流行目标检测器进行了实验。结果表明，我们的方法优于最先进的主动学习方法，验证了所提方法的有效性。