Industrial part key point detection method based on deep learning

A technology of deep learning and detection methods, which is applied to computer parts, instruments, character and pattern recognition, etc., can solve the problems of sensitivity to image transformation and environment transformation, insufficient stability and robustness, and great image quality constraints. Achieve the effects of increasing generalization ability, reducing regression difficulty, and improving robustness

Active Publication Date: 2020-01-17
青岛奥利普奇智智能工业技术有限公司
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  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, there are defects: the traditional method is greatly restricted by the image quality, and different shadows, deformations, and rotations will have a great impact on the detection and descriptor

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  • Industrial part key point detection method based on deep learning
  • Industrial part key point detection method based on deep learning
  • Industrial part key point detection method based on deep learning

Examples

Experimental program
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Example Embodiment

[0045] Example:

[0046] follow first figure 1 In order to train a network that can detect key points, after obtaining the key point network, according to figure 2 In the order of , use the multi-scale feature map fusion in the key point detection network obtained from the previous training to detect the key points, and then use the key points to match and calibrate the industrial parts through the loss function, such as Figure 4 , Figure 5 and Image 6 As shown, it is the part diagram, thermal diagram and key point diagram applied to the key point detection of the bearing workpiece.

[0047]Using a deep neural network for feature extraction, compared to traditional feature extraction methods, can better deal with the effects of lighting, deformation, rotation, etc., and in addition to the explicit features of the image, the deep neural network can implicitly Learning deeper features can improve the robustness of the overall algorithm.

[0048] Using multi-scale featur...

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Abstract

The invention discloses an industrial part key point detection method based on deep learning in the field of industrial vision. The method specifically comprises the following steps that S1, a key point detection deep neural network is trained, specifically, the key point detection deep neural network composed of three sub-networks is constructed, each sub-network comprises a plurality of 3 * 3 convolution kernels with the step length ranging from 1 to 2, and feature fusion is conducted between the sub-networks through the multi-size feature map fusion technology; S2, outputting a to-be-detected image to the training key point detection deep neural network, key points are detected by using the key point detection deep neural network obtained by training; the method is applied to a convolutional neural network structure for detecting the key points of the bearing workpart, and the network structure comprises a novel feature map fusion technology and a target loss function, so that the key point detection accuracy can be effectively improved.

Description

technical field [0001] The invention relates to the technical field of industrial vision, in particular to a method for detecting key points of industrial parts based on deep learning. Background technique [0002] For the current key point matching technology based on traditional images, for image processing technology, key point matching technology is often needed to find the affine transformation relationship between two pictures. First, the corner detection algorithm is usually used to detect key points through the pixel gray value; then, each key point is described by using local descriptors, such as SIFT, ORB, etc. In this way, a one-to-one pairing relationship between key points and descriptors is formed. When looking for an affine relationship, first detect the key points and their descriptors on the current picture, and then use the descriptors to find the closest point of the key points in the two pictures to form a point pair. Through a large number of point pair...

Claims

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Application Information

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IPC IPC(8): G06K9/46G06K9/62
CPCG06V10/462G06F18/253
Inventor 张发恩刘洋黄家水唐永亮
Owner 青岛奥利普奇智智能工业技术有限公司
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