  The ocean front has an important impact in many areas, it is meaningful to
obtain accurate ocean front positioning, therefore, ocean front detection is a
very important task. However, the traditional edge detection algorithm does not
detect the weak edge information of the ocean front very well. In response to
this problem, we collected relevant ocean front gradient images and found
relevant experts to calibrate the ocean front data to obtain groundtruth, and
proposed a weak edge identification nets(WEIN) for ocean front detection.
Whether it is qualitative or quantitative, our methods perform best. The method
uses a welltrained deep learning model to accurately extract the ocean front
from the ocean front gradient image. The detection network is divided into
multiple stages, and the final output is a multi-stage output image fusion. The
method uses the stochastic gradient descent and the correlation loss function
to obtain a good ocean front image output.
