SFEMM: A cotton binocular matching method based on YOLOv7x-Reference-Cited by-同舟云学术

SFEMM: A cotton binocular matching method based on YOLOv7x

Published:2024 Issue:3 Volume:21 Page:3618-3630
ISSN:1551-0018
Container-title:Mathematical Biosciences and Engineering
language:
Short-container-title:MBE

Author:

Guohui Zhang,Tohti Gulbahar,Ping Chen,Geni Mamtimin,Yixuan Fan

Abstract

<abstract> <p>The cotton-picking robot needs to locate the target object in space in the process of picking in the field and other outdoor strong light complex environments. The difficulty of this process was binocular matching. Therefore, this paper proposes an accurate and fast binocular matching method. This method used the deep learning model to obtain the position and shape of the target object, and then used the matching equation proposed in this paper to match the target object. Matching precision of this method for cotton matching was much higher than that of similar algorithms. It was 54.11, 45.37, 6.15, and 12.21% higher than block matching (BM), semi global block matching (SGBM), pyramid stereo matching network (PSMNet), and geometry and context for deep stereo regression (GC-net) respectively, and its speed was also the fastest. Using this new matching method, the cotton was matched and located in space. Experimental results show the effectiveness and feasibility of the algorithm.</p> </abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine

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