Bionic compound eye microlens technology-based 3-3-2 dimension object detection method and system

Abstract

The invention relates to a bionic compound eye microlens technology-based 3-3-2 dimension object detection method and system. Through a bionic compound eye microlens system-based low-resolution data acquisition method, an object region is subjected to capture imaging, according to microlens array images taken by two microlens devices, constructing a low-resolution image by a linear weighted average method, reconstructing a 3D profile of an object by a forward intersection measurement method, if the object image is effectively captured and is shown in the low-resolution image, reconstructing a high-resolution image of the object region by the microlens array images as base data by a regularization method, and after the high-resolution 2D image of the object region is acquired, carrying out precise recognition on the object by a texture gradient-based GAC model. Through addition of a process for acquiring the 3D profile of an object by a low-resolution image, redundant image unmeaning processing is effectively avoided and real-time processing efficiency and accuracy of the system are improved.

Description

technical field [0001] The present invention relates to a method and system for improving target detection efficiency, especially a 3-3-2 dimensional target detection method and system based on bionic compound eye microlens technology, that is, a two-level resolution data acquisition mode using the microlens system A high-precision and high-efficiency target detection method and system for low-resolution three-dimensional contour capture and high-resolution two-dimensional staring imaging of a target. Background technique [0002] The traditional target detection method is based on a single imaging device to shoot the target, and target detection is performed on a large number of acquired high-resolution images. The more images captured by the camera, the higher the imaging resolution of the camera means the greater the amount of data acquired and the more data information. This results in longer and less efficient computer time for automated processing. In addition, the u...

AI Technical Summary

Problems solved by technology

Although the algorithm proposed by researchers can improve the target detection efficiency of the algorithm, the massive redundant data leads to low real-time processing efficiency of the system

How to avoid meaningless processing of redundant images, research at home and abroad is almost blank

Images