  In this paper, we propose a novel data augmentation technique (ANDA) applied
to the Salient Object Detection (SOD) context. Standard data augmentation
techniques proposed in the literature, such as image cropping, rotation,
flipping, and resizing, only generate variations of the existing examples,
providing a limited generalization. Our method has the novelty of creating new
images, by combining an object with a new background while retaining part of
its salience in this new context; To do so, the ANDA technique relies on the
linear combination between labeled salient objects and new backgrounds,
generated by removing the original salient object in a process known as image
inpainting. Our proposed technique allows for more precise control of the
object's position and size while preserving background information. Aiming to
evaluate our proposed method, we trained multiple deep neural networks and
compared the effect that our technique has in each one. We also compared our
method with other data augmentation techniques. Our findings show that
depending on the network improvement can be up to 14.1% in the F-measure and
decay of up to 2.6% in the Mean Absolute Error.
