Industrial part key point detection method based on deep learning

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...

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 of key points, which means that the traditional method is easy to form a wrong match, and the image transformation and Environmental changes are very sensitive, and the stability and robustness are not enough

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