Micro-operating system target detection method

Abstract

The invention discloses a micro-operating system target detection method, including the steps of using a depth residual error convolutional neural network to perform characteristic extraction on a sample image to obtain sample characteristic graphs; using a region proposal network to perform convolution operation on the sample characteristic graphs to obtain sample target candidate boxes; using anon-line difficult sample mining method to screen the sample target candidate boxes to obtain new sample target candidate boxes, and using the sample characteristic graphs and the new sample target candidate boxes as training samples of a fully connected classification network to complete training of the fully connected classification network; and applying the depth residual error convolutional neural network to an image to be identified to obtain characteristic graphs, combined with the region proposal network, obtaining target candidate boxes, and through an area-of-interest pooling layer and the fully connected classification network that is trained, obtaining a target identification result. The micro-operating system target detection method provided by the invention is applied to target detection in the micro-operating system, can effectively position and identify each object, and at the same time, ensures requirements for an accuracy rate and real-time performance.

Description

technical field [0001] The invention belongs to the technical field of computer vision and deep learning, and more specifically relates to a micro-operating system object detection method. Background technique [0002] Micro-assembly or micro-operating system is an indispensable and important tool for people to explore the microscopic world. It has a wide range of applications in the fields of micro-part assembly, microsurgery, high-precision optical device manufacturing, and microelectronic integrated circuit manufacturing. The micro-operating system is generally composed of a micro-vision subsystem, a control subsystem, and a micro-operation actuator subsystem. The control subsystem is mainly responsible for the movement of the micro-operation actuator, the clamping of target objects, etc. Acquire information about the micromanipulation environment in a non-contact manner, such as identification of relevant components or target areas, and location information. The target ...

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Problems solved by technology

[0004] Aiming at the above defects or improvement needs of the prior art, the present invention provides a micro-operating system object detection method, thereby solving the existing problems in the prior art that the network performance will decrease when the number of positive and negative samples is uneven, and for partially occluded objects and The technical problem that targets of different attitudes cannot be effectively recognized and the real-time performance of the recognition is poor

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