Multi-View Jujube Tree Trunks Stereo Reconstruction Based on UAV Remote Sensing Imaging Acquisition System

Abstract

High-quality agricultural multi-view stereo reconstruction technology is the key to precision and informatization in agriculture. Multi-view stereo reconstruction methods are an important part of 3D vision technology. In the multi-view stereo 3D reconstruction method based on deep learning, the effect of feature extraction directly affects the accuracy of reconstruction. Aiming at the actual problems in orchard fruit tree reconstruction, this paper designs an improved multi-view stereo structure based on the combination of remote sensing and artificial intelligence to realize the accurate reconstruction of jujube tree trunks. Firstly, an automatic key frame extraction method is proposed for the DSST target tracking algorithm to quickly recognize and extract high-quality data. Secondly, a composite U-Net feature extraction network is designed to enhance the reconstruction accuracy, while the DRE-Net feature extraction enhancement network improved by the parallel self-attention mechanism enhances the reconstruction completeness. Comparison tests show different levels of improvement on the Technical University of Denmark (DTU) dataset compared to other deep learning-based methods. Ablation test on the self-constructed dataset, the MVSNet + Co U-Net + DRE-Net_SA method proposed in this paper improves 20.4% in Accuracy, 12.8% in Completion, and 16.8% in Overall compared to the base model, which verifies the real effectiveness of the scheme.

Accurate Recognition of Jujube Tree Trunks Based on Contrast Limited Adaptive Histogram Equalization Image Enhancement and Improved YOLOv8

“…In our future work, we will focus on utilizing this improved object detection model in conjunction with key frame detection algorithms to realize the deployment of nextgeneration AI IoT systems in embedded devices. For example, this may be achieved by automatically extracting multi-view views of jujube trees and constructing spatial 3D models of jujube trees based on them [40], as well as pioneering applications for more efficient and diverse agricultural information data collection in the context of smart agriculture.…”

Hash Encoding and Brightness Correction in 3D Industrial and Environmental Reconstruction of Tidal Flat Neural Radiation

“…Innovative techniques such as RTK or PPK aero photogrammetric surveying have yielded numerous accurate 3D models [ 7 , 17 ]. With advancements in photogrammetry, methods for generating dense point clouds and constructing 3D triangular grid models from 2D images have evolved, incorporating sparse reconstruction (structure from motion, SFM) [ 18 , 19 ] and dense reconstruction (multiple-view stereo, MVS) [ 20 , 21 ]. This progress has effectively transformed the reconstruction of 3D building models into a practical reality.…”