2,844 research outputs found
Using Deep Networks for Drone Detection
Drone detection is the problem of finding the smallest rectangle that
encloses the drone(s) in a video sequence. In this study, we propose a solution
using an end-to-end object detection model based on convolutional neural
networks. To solve the scarce data problem for training the network, we propose
an algorithm for creating an extensive artificial dataset by combining
background-subtracted real images. With this approach, we can achieve precision
and recall values both of which are high at the same time.Comment: To appear in International Workshop on Small-Drone Surveillance,
Detection and Counteraction Techniques organised within AVSS 201
Enhancing Infrastructure Safety: A UAV-Based Approach for Crack Detection
The imperative task of identifying and promptly detecting cracks in concrete bridges is crucial for preserving their structural health and ensuring the safety of users. Traditional bridge inspection methods heavily rely on human eyes and additional tools, demanding extensive training for inspectors and resulting in time-consuming processes. The increasing demand for Unmanned Aerial Vehicles (UAVs) has provided a transformative solution to access hard-to-reach areas efficiently. This research explores the integration of deep learning algorithms, including CNN, RCNN, Fast RCNN, Faster RCNN, and YOLO, to enhance the accuracy and efficiency of UAV-based crack detection systems. Experimental results affirm the effectiveness of these algorithms in addressing challenges such as lighting variations and small crack detection. The study aims to contribute to structural health monitoring, improving maintenance practices, and enhancing safety
A review of UAV Visual Detection and Tracking Methods
This paper presents a review of techniques used for the detection and
tracking of UAVs or drones. There are different techniques that depend on
collecting measurements of the position, velocity, and image of the UAV and
then using them in detection and tracking. Hybrid detection techniques are also
presented. The paper is a quick reference for a wide spectrum of methods that
are used in the drone detection process.Comment: 10 page
Managing crops from UAV images with data processing and Deep Learning techniques
Deep Learning is the state-of-the-art of Artificial Intelligence. Companies around the world put this technology to practical use. Accordingly, this work pretends to investigate the benefits of its use in agriculture in combination with unmanned aerial vehicles. The report introduces Deep Learning by giving its definition and the inspiration behind this technology. Further, its functioning was studied to gain an understanding of how it is applied to real-world tasks and what variations of Deep Learning exist. Accordingly, the suitable Deep Learning algorithm and its implementation was selected to fulfill the objectives of this project. The report proceeds with explaining the computer code that was written to utilize an already existing implementation and the problems that were encountered. Finally, the discussion of the accomplished results is presented along with the conclusions of the entire project and comments on future works
Machine learning to detect marine animals in UAV imagery: effect of morphology, spacing, behaviour and habitat
Machine learning algorithms are being increasingly used to process large volumes of wildlife imagery data from unmanned aerial vehicles (UAVs); however, suitable algorithms to monitor multiple species are required to enhance efficiency. Here, we developed a machine learning algorithm using a low-cost computer. We trained a convolutional neural network and tested its performance in: (1) distinguishing focal organisms of three marine taxa (Australian fur seals, loggerhead sea turtles and Australasian gannets; body size ranges: 0.8–2.5 m, 0.6–1.0 m, and 0.8–0.9 m, respectively); and (2) simultaneously delineating the fine-scale movement trajectories of multiple sea turtles at a fish cleaning station. For all species, the algorithm performed best at detecting individuals of similar body length, displaying consistent behaviour or occupying uniform habitat (proportion of individuals detected, or recall of 0.94, 0.79 and 0.75 for gannets, seals and turtles, respectively). For gannets, performance was impacted by spacing (huddling pairs with offspring) and behaviour (resting vs. flying shapes, overall precision: 0.74). For seals, accuracy was impacted by morphology (sexual dimorphism and pups), spacing (huddling and creches) and habitat complexity (seal sized boulders) (overall precision: 0.27). For sea turtles, performance was impacted by habitat complexity, position in water column, spacing, behaviour (interacting individuals) and turbidity (overall precision: 0.24); body size variation had no impact. For sea turtle trajectories, locations were estimated with a relative positioning error of <50 cm. In conclusion, we demonstrate that, while the same machine learning algorithm can be used to survey multiple species, no single algorithm captures all components optimally within a given site. We recommend that, rather than attempting to fully automate detection of UAV imagery data, semi-automation is implemented (i.e. part automated and part manual, as commonly practised for photo-identification). Approaches to enhance the efficiency of manual detection are required in parallel to the development of effective implementation of machine learning algorithms
A Comprehensive Review of AI-enabled Unmanned Aerial Vehicle: Trends, Vision , and Challenges
In recent years, the combination of artificial intelligence (AI) and unmanned
aerial vehicles (UAVs) has brought about advancements in various areas. This
comprehensive analysis explores the changing landscape of AI-powered UAVs and
friendly computing in their applications. It covers emerging trends, futuristic
visions, and the inherent challenges that come with this relationship. The
study examines how AI plays a role in enabling navigation, detecting and
tracking objects, monitoring wildlife, enhancing precision agriculture,
facilitating rescue operations, conducting surveillance activities, and
establishing communication among UAVs using environmentally conscious computing
techniques. By delving into the interaction between AI and UAVs, this analysis
highlights the potential for these technologies to revolutionise industries
such as agriculture, surveillance practices, disaster management strategies,
and more. While envisioning possibilities, it also takes a look at ethical
considerations, safety concerns, regulatory frameworks to be established, and
the responsible deployment of AI-enhanced UAV systems. By consolidating
insights from research endeavours in this field, this review provides an
understanding of the evolving landscape of AI-powered UAVs while setting the
stage for further exploration in this transformative domain
- …