  Object detection is a very important basic research direction in the field of
computer vision and a basic method for other advanced tasks in the field of
computer vision. It has been widely used in practical applications such as
object tracking, video behavior recognition and underwater robotics vision. The
Cascade-RCNN and Deformable Convolution Network are both classical and
excellent object detection algorithms. In this report, we evaluate our
Cascade-DCN based method on underwater optical image and acoustics image
datasets with different engineering tricks and augumentation.
