Automatic Fine Recognition of Ocean Front Fused with Deep Learning
摘要
Traditional oceanic front identification depends on the gradient threshold,and sea areas with a gradient value greater than the set threshold are regarded as ocean fronts.However,the threshold is set artificially according to inconsistent standards,and complex ocean fronts cannot be accurately identified based on a single threshold.To address the problems,this paper proposes an adaptive gradient threshold recognition method for ocean fronts based with deep learning.It annotates the sea temperature gradient map,and obtains a model that can identify ocean fronts at the pixel level through Mask R-CNN training.The unique gradient value distribution of each type of front is counted as the benchmark gradient threshold of the front,and based on this threshold the pixel-level front recognition results are finely adjusted.The accuracy of the front recognition results is quantified to improve the reliability of adaptive front adjustment process.Experimental results show that compared with the traditional gradient threshold method and pure deep learning,this method can realize fine ocean front recognition automatically,and has good independence and integrity.