南京理工大学学报（自然科学版）2026，Vol.50Issue(3)：274-282,9.DOI:10.14177/j.cnki.32-1397n.2026.50.03.004
基于梯度和模糊监督的边缘检测
Edge detection based on gradient and blur supervision
束家琪 1代龙泉1
作者信息
- 1. 南京理工大学 计算机科学与工程学院,江苏 南京 210094
- 折叠
摘要
Abstract
Currently,edge detection models have reached the level of human perception of edges,but models with excellent perception have a larger number of parameters and slower inference speeds,making them difficult to meet the needs in practical scenarios.Lightweight models have characteristics of small parameter sizes and fast inference speeds,but due to the limitation of model parameter numbers,they lag behind the afore-mentioned models in the comprehensive performance of precision and recall.This paper notes that existing methods use supervised signals in an insufficient and unreasonable manner during model training and accordingly proposes a training strategy of gradient and blur supervision.In training,the gradient information from labeled images is added as additional supervised signals to achieve more sufficient use of supervised signal,and the operation of blurring deep supervised signals aims to achieve a more reasonable supervision for different stages of the model.This paper applies the proposed method to models and achieve further improvements in the comprehensive performance of precision and recall of lightweight model without degrading model performance.关键词
计算机视觉/边缘检测/深度学习/轻量级模型Key words
computer vision/edge detection/deep learning/lightweight model分类
信息技术与安全科学引用本文复制引用
束家琪,代龙泉..基于梯度和模糊监督的边缘检测[J].南京理工大学学报（自然科学版）,2026,50(3):274-282,9.
