Open AccessMask R-CNN-based Cat Class Recognition and Segmentation
Author(s) -
Yile Dai,
Yunqing Liu,
Siyuan Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1966/1/012010
Subject(s) - segmentation , artificial intelligence , computer science , pattern recognition (psychology) , image segmentation , feature (linguistics) , scale space segmentation , channel (broadcasting) , segmentation based object categorization , feature extraction , computer vision , computer network , philosophy , linguistics
Aiming at the low accuracy of the traditional Mask R-CNN applied to the image segmentation of different cats, an improved Mask R-CNN recognition and segmentation algorithm was proposed. The third channel of the FPN feature extraction path is added to obtain more comprehensive feature information, improve the accuracy of the segmentation mask and reduce the training time. The experimental results show that the method achieves 87.54% segmentation accuracy on the Kaggle dog and cat classification detection dataset, which is 13.57% better than the accuracy of the traditional Mask R-CNN algorithm on the same dataset, and has better detection and segmentation performance, providing a new method for the study of instance segmentation.