Refined Edge Detection Method Based on Semantic Information

Edge detection is to accurately extract visually significant edge pixels from the image to obtain the edge information of the image.Traditional edge detection methods based on Full Convolution Network(FCN) usually require rough and fuzzy edge prediction.This paper proposes a refined edge detection method guided by semantic information.The learned image semantic information is transmitted to the edge detection subnetwork through the image segmentation subnetwork.The image semantic information is used to guide the edge detection subnetwork.A feature fusion module with attention mechanism and residual structure is also introduced to generate fine image edges to enhance feature fusion at different scales.On this basis, the cost function in image segmentation task is combined with the image edge detection task, to define a new model cost function, which is further trained to improve the quality of network edge detection.The experimental results on the BSDS500 dataset verify the effectiveness of the proposed method.The optimal dataset scale and image optimal scale attained by this method are 0.818 and 0.841, respectively.Compared with mainstream edge detection methods, such as HED and RCF, the proposed method can predict finer edge images with improved robustness.