  Complicated underwater environments bring new challenges to object detection,
such as unbalanced light conditions, low contrast, occlusion, and mimicry of
aquatic organisms. Under these circumstances, the objects captured by the
underwater camera will become vague, and the generic detectors often fail on
these vague objects. This work aims to solve the problem from two perspectives:
uncertainty modeling and hard example mining. We propose a two-stage underwater
detector named boosting R-CNN, which comprises three key components. First, a
new region proposal network named RetinaRPN is proposed, which provides
high-quality proposals and considers objectness and IoU prediction for
uncertainty to model the object prior probability. Second, the probabilistic
inference pipeline is introduced to combine the first-stage prior uncertainty
and the second-stage classification score to model the final detection score.
Finally, we propose a new hard example mining method named boosting
reweighting. Specifically, when the region proposal network miscalculates the
object prior probability for a sample, boosting reweighting will increase the
classification loss of the sample in the R-CNN head during training, while
reducing the loss of easy samples with accurately estimated priors. Thus, a
robust detection head in the second stage can be obtained. During the inference
stage, the R-CNN has the capability to rectify the error of the first stage to
improve the performance. Comprehensive experiments on two underwater datasets
and two generic object detection datasets demonstrate the effectiveness and
robustness of our method.
