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Updated: Jun 29, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
Published on: January 18, 2020
SFEMM: A cotton binocular matching method based on YOLOv7x
Zhang Guohui1, Gulbahar Tohti1, Chen Ping1
1School of Mechanical Engineering, Xinjiang University, Urumqi, Xinjiang 830047, China.
Abstract:
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.
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