  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.
