Related papers: Combined Person Classification with Airborne Optic…

The safe operation of drone swarms beyond visual line of sight requires multiple safeguards to mitigate the risk of collision between drones flying in close-proximity scenarios. Cooperative navigation and flight coordination strategies that…

Both humans and computational methods struggle to discriminate the depths of objects hidden beneath foliage. However, such discrimination becomes feasible when we combine computational optical synthetic aperture sensing with the human…

Automatic detection of flying drones is a key issue where its presence, especially if unauthorized, can create risky situations or compromise security. Here, we design and evaluate a multi-sensor drone detection system. In conjunction with…

Amodal recognition is the ability of the system to detect occluded objects. Most SOTA Visual Recognition systems lack the ability to perform amodal recognition. Few studies have achieved amodal recognition through passive prediction or…

Drone detection is a challenging object detection task where visibility conditions and quality of the images may be unfavorable, and detections might become difficult due to complex backgrounds, small visible objects, and hard to…

This paper presents an advanced mapping system that combines drone imagery with machine learning and computer vision to overcome challenges in speed, accuracy, and adaptability across diverse terrains. By automating processes like feature…

Modern Unmanned Aerial Vehicles (UAV) equipped with cameras can play an essential role in speeding up the identification and rescue of people who have fallen overboard, i.e., man overboard (MOB). To this end, Artificial Intelligence…

This work contributes to efforts on autonomously detecting a vegetation-occluded target by airborne observers. It investigates and enhances previous work on a Particle Swarm Optimization (PSO) strategy for Airborne Optical Sectioning (AOS)…

Analyzing the cone photoreceptor pattern in images obtained from the living human retina using quantitative methods can be crucial for the early detection and management of various eye conditions. Confocal adaptive optics scanning light…

UAVs are playing an increasingly important role in the field of wilderness rescue by virtue of their flexibility. This paper proposes a fusion of UAV vision technology and satellite image analysis technology for active wildfires detection…

Deep learning-based algorithms can provide state-of-the-art accuracy for remote sensing technologies such as unmanned aerial vehicles (UAVs)/drones, potentially enhancing their remote sensing capabilities for many emergency response and…

Personal monitoring devices such as cyclist helmet cameras to record accidents or dash cams to catch collisions have proliferated, with more companies producing smaller and compact recording gadgets. As these devices are becoming a part of…

Integrated sensing and communication (ISAC) boosts network efficiency by using existing resources for diverse sensing applications. In this work, we propose a cell-free massive MIMO (multiple-input multiple-output)-ISAC framework to detect…

To determine the 3D orientation and 3D location of objects in the surroundings of a camera mounted on a robot or mobile device, we developed two powerful algorithms in object detection and temporal tracking that are combined seamlessly for…

In order to respond effectively in the aftermath of a disaster, emergency services and relief organizations rely on timely and accurate information about the affected areas. Remote sensing has the potential to significantly reduce the time…

Reliable detection, localization and identification of small drones is essential to promote safe, secure and privacy-respecting operation of Unmanned-Aerial Systems (UAS), or simply, drones. This is an increasingly challenging problem with…

Trapped human detection in search and rescue (SAR) scenarios poses a significant challenge in pervasive computing. This study addresses this issue by leveraging machine learning techniques, given their high accuracy. However, accurate…

The rapid proliferation of airborne platforms, including commercial aircraft, drones, and UAVs, has intensified the need for real-time, automated threat assessment systems. Current approaches depend heavily on manual monitoring, resulting…

Mixture models are well-established learning approaches that, in computer vision, have mostly been applied to inverse or ill-defined problems. However, they are general-purpose divide-and-conquer techniques, splitting the input space into…

The growing ubiquity of drones has raised concerns over the ability of traditional air-space monitoring technologies to accurately characterise such vehicles. Here, we present a CNN using a decision tree and ensemble structure to fully…