Weak Edge Identification Nets for Ocean Front Detection
This work addresses ocean front detection, which is important for applications in oceanography and related fields, but it appears incremental as it builds on existing deep learning methods for a specific domain.
The paper tackles the problem of detecting ocean fronts by addressing the limitation of traditional edge detection algorithms in capturing weak edge information, proposing a Weak Edge Identification Nets (WEIN) that achieves the best performance both qualitatively and quantitatively.
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.