Image detection and recognition method and device

Abstract

The embodiment of the invention provides an image detection and recognition method and device, and the image detection and recognition method comprises the steps: inputting the aerial image data of anunmanned plane into a network model of a Faster-RCNN, and obtaining the accurate position of a target object outputted by the network model of the Faster-RCNN, wherein the network model of the Faster-RCNN is obtained by training the classification probability and the frame regression according to the detection classification probability and the detection frame regression. Image data collected byan unmanned aerial vehicle platform is combined based on an image recognition technology; a target detection algorithm in deep learning is applied and a learning model with a specific depth is trainedso that accurate positioning and identification of the target object can be realized; and the complex image preprocessing process can be reduced, and the detection efficiency is high, and the detection precision is high, and the practicality is high, and the adaptability and the reliability are great, and the target object can be rapidly detected and identified.

Description

technical field [0001] The invention relates to the field of computers, in particular to an image detection and recognition method and device. Background technique [0002] With the progress of the times and the development of the industry, the development of the fruit tree industry has gradually shown a trend of large-scale, modern and intelligent development. The precise positioning and identification of fruit trees plays an active role in intelligent orchard management and pest monitoring, and can provide more reliable data for drone spraying technology in modern orchards. In modern large-scale orchards, the precise positioning and identification of fruit trees is also very important for the growth analysis of each fruit tree. The existing large-area fruit tree positioning and identification methods generally use manual field surveys or use geographic information systems and satellite remote sensing image processing technologies. However, due to the continuous expansion ...

AI Technical Summary

Problems solved by technology

The existing large-area fruit tree positioning and identification methods generally use manual field surveys or use geographic information systems and satellite remote sensing image processing technologies. However, due to the continuous expansion of modern orchards, relying solely on manual field surveys is time-consuming and laborious; Due to the low spatial resolution of the image, the positioning and recognition accuracy is low and the real-time performance is not strong

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