Research on fine-grained visual classification based on salient object detection

Abstract In response to the current algorithm of fine-grained image classification is greatly disturbed by complex backgrounds with similar features to the objects to be classified, salient object detection is introduced into fine-grained visual classification (FGVC), and a FGVC algorithm based on saliency target detection is proposed in this paper. The target object and background of the original image are segmented using the saliency map obtained from the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:msup> <mml:mrow> <mml:mtext>U</mml:mtext> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> <mml:mrow> <mml:mtext>Net</mml:mtext> </mml:mrow> </mml:mrow> </mml:math> salient object detection model. The images with background interference removed are fed into the EfficientNet image classification network to achieve classification. Experiments show that this method has better classification results compared with the original image directly into EfficientNet and the EfficientNet with attention mechanism. The parameters are tuned on the base EfficientNet to obtain higher classification accuracy.