A context copy-paste data augmentation method and system for multi-class remote sensing target detection and a storage medium

Abstract

This invention discloses a context-based copy-paste data augmentation method, system, and storage medium for multi-class remote sensing target detection, belonging to the field of image processing and target detection technology. The method is implemented through the following steps: First, prepare a multi-class remote sensing dataset and set hyperparameters; second, generate copy regions of spatial context, using the DBSCAN clustering method to preserve target spatial context information; next, generate paste regions of semantic context, using a ResNet50 network to extract background feature vectors and match candidate paste regions; finally, perform the paste operation and update the annotation information. This invention generates copy regions through clustering, preserves spatial context, and matches paste regions based on semantic context, making the augmented image closer to the real image. It effectively alleviates the long-tail problem of datasets, enhances the network's learning balance, improves detection performance in low-sample classes and complex scenes, and can be seamlessly integrated with various remote sensing target detectors.

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