Image Semantic Segmentation Fusion of Edge Detection and AFF Attention Mechanism
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Published:2022-11-06
Issue:21
Volume:12
Page:11248
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Jiao Yijie,Wang Xiaohua,Wang Wenjie,Li Shuang
Abstract
Deep learning has been widely used in various fields because of its accuracy and efficiency. At present, the improvement of image semantic segmentation accuracy has become the area of most concern. In terms of increasing accuracy, improved semantic segmentation models have attracted more attention. In this paper, a hybrid model is proposed to solve the problems of edge splitting and small objects disappearing from complex scene images. The hybrid model consists of three parts: (1) an improved HED network, (2) an improved PSP-Net, (3) an AFF attention mechanism. Continuous edges can be obtained by combining the improved HED network with an improved PSP-Net. The AFF attention mechanism can improve the segmentation effect of small target objects by enhancing its response recognition ability for specific semantic scenes. The experiments were carried out on Cityspaces, SIFT Flow, NYU-V2 and CamVid datasets, and the experimental results show that the segmentation accuracy of our method is improved by 2% for small target objects, and by 3% for scenes with complex object edges.
Funder
Natural Science Foundation of China Key Research and Development plan of Shaanxi province China Innovation Capability Support Program of Shaanxi Key Research and Development program of Shaanxi Province
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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