Research on the network substitution effect of Mask R-CNN algorithm for fuzzy input images

Jiang Shan

doi:10.1117/12.2671332

28 April 2023 Research on the network substitution effect of Mask R-CNN algorithm for fuzzy input images

Jiang Shan

Author Affiliations +

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 126101Y (2023) https://doi.org/10.1117/12.2671332
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

Mask R-CNN is a relatively mature method for instance segmentation at this stage, aiming at the problems of segmentation boundary accuracy and poor robustness to blurry pictures in the Mask R-CNN algorithm, an improved Mask R-CNN instance segmentation method is proposed. Use the SegNeXt network structure to optimize the mask branch for further segmentation of candidate regions, and then use new anchor size and IOU standards so that the candidate box can cover all instance regions. Finally, a method is used to add partially transformed data from a transformation network for training. Compared with the original algorithm, the total mAP value is increased by 2.2%, and the accuracy and robustness of the segmentation boundary are improved.

Citation Download Citation

Jiang Shan "Research on the network substitution effect of Mask R-CNN algorithm for fuzzy input images", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 126101Y (28 April 2023); https://doi.org/10.1117/12.2671332

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