In response to the significant difficulty in detecting extremely small targets in aerialremote sensing images, this research proposes a data enhancement method that optimizes the negative example selection strategy, that is, to achieve the objective by optimizing the strategy of selecting negative samples for training during the training process on the basis of existing deep learning-based target detection methods. Taking the Faster-RCNN model as an example, we use the outcome of model error identification as the negative example in the next epoch of the training process and adjusts the loss function according to the negative example category. Experimental shows that the algorithm can effectively enhance the detection accuracy of extremely small targets in remote sensing images, with a strong screening ability for interference regions in complicated backgrounds.
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