Unmanned-aerial-vehicle low-altitude-target accurate detection identification method

Abstract

The invention discloses an unmanned-aerial-vehicle low-altitude-target accurate detection identification method. According to fully convolutional networks (FCN), unmanned-aerial-vehicle low-altitude-target accurate detection identification based on a scale estimation model is realized. In the method, according to a characteristic that a low-altitude-target vehicle, a motorcycle, a cyclist and a pedestrian have obvious scale ranges, through the model, the pixel scale of a target is calculated, simultaneously the parameter of an anchor is calculated too and identification precision is increased.

Description

technical field [0001] The invention belongs to the fields of computer vision and artificial intelligence. Specifically, it relates to an accurate detection and recognition method for low-altitude targets of an unmanned aerial vehicle, which is used to improve the detection and recognition accuracy and speed of low-altitude targets. technical background [0002] Object detection requires locating the location of the object and identifying the class of the object. At present, general target detection is mainly based on R-CNN, Faster-RCNN, R-FCN, and R-FCN-3000 frameworks. The core idea of ​​this series of frameworks is to select candidate frames on the image and learn through CNN (Convolutional Neural Network). The R-CNN framework achieved an average accuracy (mean Average Precision, mAP) of 58.5% in the VOC2007 data set test, and the Faster-RCNN framework achieved a mAP of 73% on the VOC2007, and the NVIDIA Tesla K40GPU speed can reach 5fps (picture The number of frames t...

AI Technical Summary

Problems solved by technology

Although the speed is faster than the previous framework, the accuracy for complex datasets drops significantly

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