Automatic Calculation of Loss Rate of Cotton Pickers Based on Deep Neural Network and Image Processing

Tian, Jialue; Qian, Zhongmin; Xie, Qing Sen; Sun, Yongfei; Shi, Lei

doi:10.1145/3568199.3568219

Cited by 1 publication

(1 citation statement)

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“…In [14], a full review and analysis are developed to compare the classical and the latest techniques, taking into count the feature detection, description, and matching techniques from hand-crafted methods, and introducing the reader to typical image matching-base applications. Recent works use LoFTR, a detector-free deep learning matching model, and a matching process with MAGSAC++ estimator to propose a transformer-based feature matching approach to find the same key points from two images with different perspectives of the shot [15].…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning-Based Visual Map Generation for Mobile Robot Navigation

García-Pintos,

Aldana-Murillo,

Ovalle-Magallanes

et al. 2023

Eng

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Visual map-based robot navigation is a strategy that only uses the robot vision system, involving four fundamental stages: learning or mapping, localization, planning, and navigation. Therefore, it is paramount to model the environment optimally to perform the aforementioned stages. In this paper, we propose a novel framework to generate a visual map for environments both indoors and outdoors. The visual map comprises key images sharing visual information between consecutive key images. This learning stage employs a pre-trained local feature transformer (LoFTR) constrained with a 3D projective transformation (a fundamental matrix) between two consecutive key images. Outliers are efficiently detected using marginalizing sample consensus (MAGSAC) while estimating the fundamental matrix. We conducted extensive experiments to validate our approach in six different datasets and compare its performance against hand-crafted methods.

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Section: Related Workmentioning

confidence: 99%