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色、梯度和纹理信息来检测边缘,如手工设计的Sobel 算子。这类方法虽然计算
究人员的重点关注。以CASENet 为代表的全卷积网络是语义边缘检测的常用方
是CNN 结构难以对远距离的上下文信息进行建模,导致大量的边缘检测错误。
在传统编码器-解码器的基础上引入双歧路分支,两分支采用Transformer 结构
意力机制进行融合。实验表明,SEDTR 的检测精度达到了当前的SOTA(State
Of The Art) 水平。
函数,提升了水线检测的精度。实验表明,在包含1000 幅从黄骅港实际采集的

Other Abstract

Semantic edge detection is one of the classical research tasks in the field of

computer vision. In the early days, researchers mainly detected edges based on

the color, gradient and texture information of images, such as the hand-designed

Sobel operator. Although such methods have low computational complexity, they

are vulnerable to environmental factors and have poor robustness, which makes

it difficult to meet the detection accuracy requirements of complex scenes. In

addition, feature extractors with high dependence on manual design also limits

the performance of these methods.

In recent years, with the rapid development of deep learning technology, convolutional

neural networks have been widely applied in the field of computer vision,

and the performance of semantic edge detection has been improved qualitatively.

Traditional methods have weak anti-noise ability and lack the ability to select specific

edges, so they are only suitable for low-precision edge extraction. However,

semantic edge detection based on deep learning not only have strong robustness

but also can learn for interested edges, so they have attracted the focus of researchers.

Fully convolutional network represented by CASENet is a common

method for semantic edge detection. Such methods adopt encoder-decoder structure

to extract features of different scales while continuously down-sampling, and

restore to original resolution after feature fusion. There are two main defects of

these methods: first, a large number of image edge details are lost due to highly

down-sampling; second, CNN structure is difficult to model remote context information,

resulting in a large number of errors. To solve these problems, this

paper introduces attention mechanism and designs a new semantic edge detection

model based on the existing network research. Experiments show that the introduction

of attention mechanism can effectively enhance the modeling ability of

long distance dependence between image pixels, and thus improve the accuracy of

semantic edge detection. The main contributions of this paper are as follows:

(1) A semantic edge detection network (SEDTR) based on attention mechanism

is proposed. Based on the traditional encoder-decoder, this network intro-

duces a dual-channel branch, which uses Transformer structure to extract low-level

edge information and high-level semantic information respectively. Features are

fused by cross-branch attention mechanism before being input into the decoder.

Experimental results show that SEDTR achieves State Of The Art (SOTA) accuracy.

(2) A lightweight waterline detection network WLNet based on attention

mechanism is proposed. In this network, semantic segmentation was incorporated

into the framework of waterline detection network as an auxiliary task. Feature

extraction and feature fusion modules were designed based on attention mechanism

to enhance the model’s perception ability of waterline edge pixels. In addition,

a new loss function for waterline detection is constructed by using the continuity

of waterline and duality of dual tasks, which improves the accuracy of waterline

detection. The experiment shows that compared with the existing methods, the

proposed method can greatly improve the accuracy of waterline detection on the

dataset of 1000 water scale images collected from Huanghua Port.

Keyword语义边缘检测 注意力机制 水线检测 特征融合
Document Type学位论文
Corresponding Author陈宇航
Recommended Citation
GB/T 7714
陈宇航. 基于注意力机制的图像语义边缘检测方法研究[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,2022.
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