Target identification and capture positioning method based on deep learning

Abstract

The invention discloses a target identification and capture positioning method based on deep learning, and belongs to the field of machine vision. The method comprises the following steps that: firstly, utilizing a Kinect camera to collect the depth and the color image of a scene; then, using a Faster R-CNN (Regions with Convolutional Neural Network features) deep learning algorithm to identify ascene target; according to an identified category, selecting a captured target area as the input of a GrabCut image segmentation algorithm; through image segmentation, obtaining the outline of the target so as to obtain the specific position of the target as the input of a cascade neural network for carrying out optimal capture position detection; and finally, obtaining the capture position and the capture gesture of a mechanical arm. Through the method, the instantaneity, the accuracy and the intelligence of target identification and positioning can be improved.

Description

technical field [0001] The invention belongs to the field of machine vision. A target recognition and grasping positioning method based on deep learning is proposed to improve the real-time performance, accuracy and intelligence of target recognition and positioning. Background technique [0002] With the development of industrial automation technology, the number of robots is increasing, and industrial robots have been widely used in all aspects of life such as automobile manufacturing, machining, electrical and electronic, and smart home services. The development of robot technology reflects the development level of a country's automation to a certain extent. With the development of social economy, the expansion of production scale and the complexity of production environment, the development and production of more intelligent, information-based and high-precision automation systems Appears to be particularly important. In the field of machine vision, target recognition ...

AI Technical Summary

Problems solved by technology

The image processing of the traditional manipulator recognition stage uses the method of feature extraction to process image information. The process of feature extraction is easily affected by external factors such as illumination, target shape and target size, so the generalization ability and robustness are poor.

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