Target detection and positioning method based on YOLOv3 and OpenCV

Abstract

The invention discloses a target detection and positioning method based on YOLOv3 and OpenCV. The method comprises the following steps: acquiring an image of a to-be-detected target by using a test platform; inputting the acquired image into a YOLOv3 algorithm after the output scale of the network structure is reduced, and performing target detection by using a YOLOv3 target detection model to obtain image coordinates; and inputting the obtained image coordinates into a SolvePnP algorithm based on OpenCV, and solving world coordinates. The YOLOv3 algorithm has the characteristics of small sizeand high precision, and can effectively reduce the calculation amount of the algorithm and improve the operation speed of the algorithm by reducing the scale of the output tensor of the algorithm onthe basis of ensuring that the accuracy of the algorithm is not changed; compared with other methods, the SolvePnP spatial positioning method is simpler and more convenient, and the rotation matrix and the translation matrix of the monocular camera can be solved only by four feature points on the basis of ensuring the precision, so that the spatial position of a to-be-detected target is solved.

Description

technical field [0001] The invention relates to the technical field of object detection in the direction of computer vision, in particular to a target detection and positioning method based on YOLOv3 and OpenCV. Background technique [0002] Object Detection is one of the basic tasks in the field of computer vision. In recent years, with the rapid development of neural networks, object detection algorithms based on deep learning have also flourished. The target detection algorithm based on deep learning adopts an end-to-end solution, that is, the input image is completed in one step to output the task result, which can effectively improve the efficiency of problem solving. However, in the actual process of detecting moving targets, there will be limitations of hardware technology, so there is a lot of room for improvement in real-time performance. [0003] The task of the spatial positioning algorithm is to convert the target coordinates in the image coordinate system to th...

AI Technical Summary

Problems solved by technology

However, due to the harsh conditions in the actual application process, whether it is camera selection or bad weather and other factors, it can have a considerable impact on the accuracy of spatial positioning. Therefore, the accuracy of spatial positioning algorithms in practical applications is a thorny problem to be solved. question

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