Based on the double encoder model, the coastline detection of Liaoning coast was carried out

Aiming at coastline detection, this study proposes a novel dual-encoder edge detection framework. This method enhances edge detection by incorporating attention mechanisms that merge local features derived from traditional models with global contextual information from the Vision-Transformer network. The approach described in this paper, in conjunction with the Unet, RCF, and Swim-UNet models, utilizes an annotated coastline image dataset to conduct experiments on edge detection.The experimental results show that the proposed model has obvious advantages in accuracy, efficiency and interference immunity. The main performance is reflected in the training speed of the proposed model when trained on NVIDIA RTX-3060 GPU is faster than HED and RCF by 2.15% and 5.3%, respectively, and the pixel accuracy is improved by 3.97% compared with the Unet model. When facing more interference lines images, the proposed model also has nice results.