YOLOMask, an Instance Segmentation Algorithm Based on Complementary Fusion Network

Jiang Hua, Tonglin Hao, Liangcai Zeng and Gui Yu
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Jiang Hua: Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
Tonglin Hao: School of Automation, Wuhan University of Science and Technology, Wuhan 430081, China
Liangcai Zeng: Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
Gui Yu: Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China

Mathematics, 2021, vol. 9, issue 15, 1-12

Abstract: Object detection and segmentation can improve the accuracy of image recognition, but traditional methods can only extract the shallow information of the target, so the performance of algorithms is subject to many limitations. With the development of neural network technology, semantic segmentation algorithms based on deep learning can obtain the category information of each pixel. However, the algorithm cannot effectively distinguish each object of the same category, so YOLOMask, an instance segmentation algorithm based on complementary fusion network, is proposed in this paper. Experimental results on public data sets COCO2017 show that the proposed fusion network can accurately obtain the category and location information of each instance and has good real-time performance.

Keywords: image segmentation; deep learning; instance segmentation; fusion network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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