Deep learning-based semantic segmentation of urban-scale 3D meshes in remote sensing: A survey

This research presents a groundbreaking approach in aerial image analysis by integrating the Real-Time Detection and Recognition (RT-DETR-X) model with the Slicing Aided Hyper Inference (SAHI) methodology, utilizing the VisDrone-DET dataset. Aimed at enhancing the efficiency of drone technology across a spectrum of applications, including water conservancy, geological exploration, and military operations, this study focuses on harnessing the real-time, end-to-end object detection capabilities of RT-DETR-X. Characterized by its high-speed and high-accuracy performance, particularly in UAV aerial photography, RT-DETR-X demonstrates a remarkable 54.8% Average Precision (AP) and 74 frames per second (FPS), surpassing similar models in both speed and accuracy. The research thoroughly examines the VisDrone-DET dataset, which encompasses a diverse range of small targets in UAV aerial photography scenes. Covering 10 distinct categories, the dataset provides a robust platform for rigorous model testing. The study emphasizes the utilization of the original image dataset for comprehensive training and evaluation, alongside the practical implementation of the SAHI method for enhanced detection of small-scale objects. Through an in-depth exploration of the model’s performance in various scenarios and a detailed analysis of the environmental setup, this paper underscores the impact of integrating RT-DETR with the SAHI approach. The findings reveal significant progress in drone detection technologies, offering a holistic framework for effective and efficient aerial surveillance. The integration not only boosts the model’s detection accuracy but also opens new avenues for advanced image analysis in UAV applications.

Enhancing UAV Aerial Image Analysis: Integrating Advanced SAHI Techniques With Real-Time Detection Models on the VisDrone Dataset

The 3D point cloud (3DPC) has significantly evolved and benefited from the advance of deep learning (DL). However, the latter faces various issues, including the lack of data or annotated data, the existence of a significant gap between training data and test data, and the requirement for high computational resources. To that end, deep transfer learning (DTL), which decreases dependency and costs by utilizing knowledge gained from a source data/task in training a target data/task, has been widely investigated. Numerous DTL frameworks have been suggested for aligning point clouds obtained from several scans of the same scene. Additionally, DA, which is a subset of DTL, has been modified to enhance the point cloud data's quality by dealing with noise and missing points. Ultimately, fine-tuning and DA approaches have demonstrated their effectiveness in addressing the distinct difficulties inherent in point cloud data. This paper presents the first review shedding light on this aspect. it provides a comprehensive overview of the latest techniques for understanding 3DPC using DTL and domain adaptation (DA). Accordingly, DTL's background is first presented along with the datasets and evaluation metrics. A well-defined taxonomy is introduced, and detailed comparisons are presented, considering different aspects such as different knowledge transfer strategies, and performance. The paper covers various applications, such as 3DPC object detection, semantic labeling, segmentation, classification, registration, downsampling/upsampling, and denoising. Furthermore, the article discusses the advantages and limitations of the presented frameworks, identifies open challenges, and suggests potential research directions.

Advancing 3D point cloud understanding through deep transfer learning: A comprehensive survey

Sub-aperture stitching in digital holography (DH) is a very important issue both for the spatial resolution improvement as well as for measuring larger aperture through synthetic enlargement of numerical aperture. In fact, sub-apertures stitching permits to greatly expand the capabilities of optical metrology thus allowing to accurately measure complex optical surfaces such as large spherical and aspheric. Stitching operations can be difficult and cumbersome depending on geometric parameters of specific objects under test. However, here we show that machine learning can definitively aid this process. In fact, here we propose for the first time, to the best of our knowledge, a novel sub-aperture stitching approach based on machine learning applied to an array of different phase-maps sub-apertures recorded by an off-axis digital holographic systems. Essentially, we construct a network according to computation model of sub-aperture stitching and remove the alignment errors and system aberration of sub-aperture maps by training the network. Correct measurement of the surface topography of hemisphere surface is demonstrated thus validating the proposed learning approach. Reported results demonstrate that machine learning can be a useful tool for simplifying the process and for making it a reliable and accurate tool in optical metrology.

Stitching sub-aperture in digital holography based on machine learning.

High-precision spherical subaperture stitching interferometry based on digital holography

3D building model generation from MLS point cloud and 3D mesh using multi-source data fusion

Subaperture stitching interferometry based on digital holography

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3D building model generation from MLS point cloud and 3D mesh using multi-source data fusion

Subaperture stitching interferometry based on digital holography