Adaptive Weighted Semantic Edge Detection of Cultural Relics

摘要

Boundary and edge cues are very useful in improving various visual tasks, such as semantic segmentation, object recognition, stereo vision, and object generation. In recent years, the issue of edge detection has been revisited, and deep learning has made significant progress. The traditional edge detection is a challenging two-category problem, and the Multi-category semantic edge detection is a more challenging problem. And we model the edge detection of cultural relics and classify the pixels of cultural relics. To this end, we propose a novel end-to-end deep semantic edge learning architecture based on ResNet. Then, we proposed an adaptive class vveighter for this problem to supervise the training. The results show that the proposed architecture is superior to the existing semantic edge detection methods in our own design of cultural relic edge detection performance.