A UAV Image Annotation Method Aided by Deep Learning Target Detection Algorithm

Abstract

The invention provides an unmanned aerial vehicle image labeling method assisted by a deep learning target detection algorithm, which belongs to the field of unmanned aerial vehicle image processing. The present invention proposes different schemes for two scenarios of unmanned image labeling: full image labeling scene, using other small amount of public drone image data sets for initial training of detection network, grouping unlabeled images according to the number of targets from less to more After forward inference, automatic processing and manual correction, the sequential input network will add this group of images to the original data set to retrain the network in order to have better detection performance for the next group of images; part of the image area is marked with the scene , the detection network is trained by using sub-images of similar size in the unlabeled area randomly cropped from the labeled area in the dataset, and then the unlabeled area is labeled. The method of the invention greatly reduces the manpower and material resources required for the unmanned aerial vehicle image labeling, and improves the labeling speed and precision.

Description

technical field [0001] The invention belongs to the field of UAV image processing, and in particular relates to an UAV image tagging method assisted by a deep learning target detection algorithm. Background technique [0002] UAV image refers to the ground remote sensing image collected by the imaging platform on the UAV. A wide range of applications, including traffic flow monitoring based on deep learning and moving target tracking, etc. This also creates a broader need for annotated drone imagery datasets. [0003] Different from conventional scene images, UAV images are often large in size, cover a wide range of land surfaces, and have a large number of typical targets. Manually labeling a UAV image often takes several minutes, which requires a large amount of labeled training data. The time cost required in the task is unbearable. [0004] In recent years, the target detection method based on deep learning has gradually matured, and the detection accuracy has been gr...

AI Technical Summary

Problems solved by technology

At present, most of the annotations for UAV image data sets are manually annotated based on relevant annotation software. The existing annotation methods lack the use of a small amount of public UAV image data to train a detection network with good generalization and then assist unlabeled Annotating technology for UAV images

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