Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
