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|
|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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