Object-level edge detection method based on deep residual network

An edge detection and residual technology, applied in neural learning methods, biological neural network models, image data processing, etc., can solve the problems of high noise and low edge resolution, and achieve high resolution, few network parameters, and less noise Effect

Active Publication Date: 2020-01-17
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0007] In order to solve the problems of low edge resolution and high noise in existing object-level edge detection methods, the present invention provides an object-level edge detection method based on a multi-scale residual network, which specifically includes the following steps

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  • Object-level edge detection method based on deep residual network
  • Object-level edge detection method based on deep residual network
  • Object-level edge detection method based on deep residual network

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Embodiment Construction

[0052] The present invention will be described in detail below in conjunction with the embodiments and accompanying drawings, but the present invention is not limited thereto.

[0053] The computer hardware configuration that the inventive method selects operation is Intel (R) Xeon (R) E5-2678 CPU@2.50GHz, and GPU is GeForce GTX TITAN Xp, and video memory is 12GB, and internal memory is 16GB; Software environment is the Ubuntu 16.04 system of 64 bits , PyTorch0.4.1 and Matlab R2017b. The detection indicators of the edge detection model mainly include: fixed contour threshold ODS (Optimal Dataset Scale, ODS), single image optimal threshold OIS (Optimal ImageScale, OIS), average precision AP (Average Precision, AP).

[0054] Such as figure 1 As shown, the object-level edge detection method based on deep convolutional neural network includes the following four parts:

[0055] (1) The construction of the neural network includes four sub-steps:

[0056] (1-1) Based on the deep ...

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Abstract

The invention relates to an object-level edge detection method based on a deep residual network. The method comprises the following four parts: (1) establishing a neural network: taking a deep residual error network as a basic network structure, replacing a convolution residual error structure in the basic network structure with a mixed cavity convolution residual error block, and adding a multi-scale feature enhancement module and a pyramid multi-scale feature fusion module; (2) performing training optimization on the neural network through data set enhancement and hyper-parameter setting; (3) completing the training of the neural network; and (4) detecting the general image by using the trained neural network, and outputting an object-level edge detection image. Compared with a traditional edge detection operator and an existing neural network edge detection method, the method has a better edge detection effect, a detection result is closer to a real value, and noise is lower.

Description

technical field [0001] The invention belongs to the field of intelligent vision processing, and in particular relates to an object-level edge detection method based on a deep residual network. Background technique [0002] Object-oriented edge detection aims to propose visually salient edges and object boundaries from natural images, and is a fundamental problem in the field of intelligent vision processing. At present, most detection methods still stay in the stage of traditional edge detection operators. These operators mainly use low-level visual features such as color gradient, brightness and texture of images to realize edge detection. Due to the high real-time performance, traditional detection methods are still widely used in many fields, but their limitations are also obvious. It is difficult to detect edges at the semantic level through low-level features, and it is difficult to be directly applied to automatic driving, 3D reconstruction, and intelligent image compr...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/13G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06T7/13G06N3/08G06T2207/20081G06T2207/20084G06V10/44G06V10/464G06N3/045G06F18/253G06F18/214
Inventor 朱威王图强陈吟凯陈悦峰何德峰郑雅羽
Owner ZHEJIANG UNIV OF TECH
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