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ORACLE: Enhancing Wildlife Surveillance with Drones

Researchers developed ORACLE, an advanced computer vision model utilizing YOLO architecture for automated bird detection and tracking from drone footage. Achieving a 91.89% mean average precision, ORACLE significantly enhances wildlife conservation by accurately identifying and monitoring avian species in dynamic environments.

20 Jun 2024

Computer Vision for Aircraft Attitude Estimation

Researchers have developed a markerless computer vision method to measure aircraft model attitudes during dynamic wind tunnel testing, providing accurate estimations without altering aerodynamic properties. This novel technique, validated through simulations and real-world data, significantly improves accuracy over traditional methods and offers versatility for various aircraft designs and applications.

20 Jun 2024

Blockchain and Drones: A Decentralized Revolution

Integrating blockchain with the Internet of Drones (IoD) promises enhanced security, connectivity, and efficiency in drone applications like delivery, surveillance, and rescue operations.

27 May 2024

Automation of Structural Health Monitoring for Civil Infrastructures Using AI and ML

This review explores the critical role of image-processing technologies in structural health monitoring (SHM) for civil infrastructures. It highlights the integration of artificial intelligence (AI) and machine learning (ML) to enhance SHM automation and accuracy. Various imaging modalities, including drones, thermography, LiDAR, and satellite imagery, are discussed for damage detection, crack identification, and deformation monitoring.

23 Apr 2024

Small Target Detection in UAV Aerial Images with a Multi-Scale Detection Network

Researchers from China proposed an innovative method to improve the accuracy of detecting small targets in aerial images captured by unmanned aerial vehicles (UAVs). By introducing a multi-scale detection network that combines different feature information levels, the study aimed to enhance detection accuracy while reducing interference from image backgrounds.

11 Apr 2024

Advancements in Image-Based Crop Yield Calculation

Researchers delve into the evolving landscape of crop-yield prediction, leveraging remote sensing and visible light image processing technologies. By dissecting methodologies, technical nuances, and AI-driven solutions, the article illuminates pathways to precision agriculture, aiming to optimize yield estimation and revolutionize agricultural practices.

14 Mar 2024

YOLOv5s-ngn: Advancing Air-to-Air UAV Detection with Enhanced Object Detection Model

This research presents YOLOv5s-ngn, a novel approach for air-to-air UAV detection addressing challenges in collision avoidance. Enhanced with lightweight feature extraction and fusion modules, alongside the EIoU loss function, YOLOv5s-ngn showcases superior accuracy and real-time performance, marking a significant advancement in vision-based target detection for unmanned aerial vehicles.

8 Feb 2024

Bayesian Framework: Ensuring Robust Autonomy in Extreme Environments

Researchers present a groundbreaking Bayesian learning framework, combining interval continuous-time Markov chain model checking, to verify autonomous robots in challenging conditions. Demonstrated on an underwater vehicle mission, the technique provides robust estimates for mission success, safety, and energy consumption, offering a scalable solution for diverse autonomous systems in uncertain environments.

5 Feb 2024

Double Camouflage Encryption of QR Codes for Secure UAV Landing

This paper introduces a double camouflage encryption method for QR codes in UAV landing scenarios to ensure information security. Utilizing a private key and carrier image, the QR code undergoes double camouflage encryption, modulating a public key based on ghost imaging principles to generate ciphertext. The decryption process involves extracting the camouflage image with the public key and decrypting the QR code with the private key.

14 Dec 2023

IoT-Integrated Drone Cybersecurity: Using DL and ML for Intrusion Detection

This paper explores the integration of IoT with drone technology to enhance data communication and security across various industries, including agriculture and smart cities. The study focuses on the use of machine learning and deep learning techniques to detect cyberattacks within drone networks and presents a comprehensive framework for intrusion detection.

2 Nov 2023

Exploring Customer Preferences for Automated Parcel Delivery Modes

This study delves into customer preferences for automated parcel delivery modes, including autonomous vehicles, drones, sidewalk robots, and bipedal robots, in the context of last-mile logistics. Using an Integrated Nested Choice and Correlated Latent Variable model, the research reveals that cost and time performance significantly influence the acceptability of technology, with a growing willingness to explore novel delivery automation when cost and time align.

1 Nov 2023

AI Revolutionizes Biodiversity Conservation and Monitoring

Researchers highlight the increasing role of artificial intelligence (AI) in biodiversity preservation and monitoring. AI is shown to be a powerful tool for efficiently processing vast datasets, identifying species through audio recordings, and enhancing conservation efforts, though concerns about its environmental impact must be addressed.

1 Nov 2023

Revolutionizing Plant Seedling Monitoring with GL-CNN and UAV Technology

Researchers introduce the Gap Layer modified Convolution Neural Network (GL-CNN) coupled with IoT and Unmanned Aerial Vehicles (UAVs) for accurate and efficient monitoring of palm tree seedling growth. This approach utilizes advanced image analysis techniques to predict seedling health, addressing challenges in early-stage plant monitoring and restoration efforts. The GL-CNN architecture achieves impressive accuracy, highlighting its potential for transforming ecological monitoring in smart farming.

15 Aug 2023

Invisible Targets Revealed: The Power of Adaptive Drone Swarms in Occluded Environments

Researchers propose an innovative real-time particle swarm optimization (PSO) technique implemented by drone swarms with synthetic aperture (SA) to improve target detection and tracking in dense forests. The adaptive swarm sampling approach using PSO outperforms blind sampling methods, providing faster and more reliable detection of occluded targets, making it highly beneficial for search and rescue missions, wildlife observation, and security patrols in challenging environments.

6 Aug 2023

TwinPort Integrates Digital Twin and Drones for Precise Ship Maneuvering

Researchers propose TwinPort, a cutting-edge architecture that combines digital twin technology and drone-assisted data collection to achieve precise ship maneuvering in congested ports. The approach incorporates a recommendation engine to optimize navigation during the docking process, leading to enhanced efficiency, reduced fuel consumption, and minimized environmental impact in smart seaports.

1 Aug 2023

Researchers Pioneer Machine-Learning Approach to Enhance Control of Robots in Dynamic Environments

Researchers from MIT and Stanford have developed a machine-learning approach to improve the control of robots, such as drones and autonomous vehicles, in rapidly changing and dynamic environments. The technique incorporates control theory structures into the learning process, enabling the robots to adapt and function more effectively in real-world scenarios.

27 Jul 2023

GeoAI Challenge Workshop: A Modern-day Moonshot for a Trillion-Pixel View of Earth

Technology experts convened at Oak Ridge National Laboratory's Department of Energy for the Trillion-Pixel GeoAI Challenge workshop to discuss the future of geospatial systems. The event emphasized advancements in artificial intelligence, cloud infrastructure, high-performance computing, and remote sensing, highlighting their potential in addressing national and human security concerns like disaster response and land-use planning.

19 Jul 2023

Smart Seas: Revolutionizing Aquaculture with AIoT and Digital Twin-Based Intelligent Fish Farming

The integration of AIoT and digital twin technology in aquaculture holds the key to revolutionizing fish farming. By combining real-time data collection, cloud computing, and AI functionalities, intelligent fish farming systems enable remote monitoring, precise fish health assessment, optimized feeding strategies, and enhanced productivity. This integration presents significant implications for the industry, paving the way for sustainable practices and improved food security.

11 Jul 2023

Revolutionizing Last-Mile Logistics: A Sustainable Approach Integrating Drones and Trucks

A groundbreaking mathematical model, the FSTSP-DR-MP, has been proposed to transform last-mile logistics into a more sustainable and efficient process. With the surge in online shopping and the subsequent rise in carbon emissions, this innovative approach integrates both delivery and return services using a combination of trucks and drones. The model optimizes routes, considering multiple payloads and customers, to minimize service time.

9 Jul 2023

Unlocking the Potential of Robotics with ChatGPT

The paper explores the use of ChatGPT in robotics and presents a pipeline for effective integration. The study demonstrates ChatGPT's proficiency in various robotics tasks, showcases the PromptCraft tool for collaborative prompting strategies, and emphasizes the potential for human-interacting robotics systems using large language models.

6 Jul 2023