Thanks to the maturity of their field, Unmanned Aerial Vehicles (UAVs) have a wide range of applications. Recently, we have witnessed an increase in the usage of multiple UAVs and UAV swarm due to their ability to achieve more complex tasks. Our goal is to use deep learning methods for object detection in order to detect and track a target drone in an image captured by another drone. In this work, we review four popular object detection categories: two-stage (anchor-based) methods, one-stage (anchor-based) methods, anchor free methods and Transformer-based methods. We compare these methods’ performance (COCO benchmark) and detection speed (FPS) for the task of real-time monocular 2D object detection between dual drones. We created a new dataset using footage from different scenes such as cities, villages, forests, highways, and factories. In our dataset, drone target bounding boxes are present at multiple scales. Our experiments show that anchor free and Transformer-based methods have the best performance. As for detection speed, the one-stage methods obtain the best results followed by and anchor free methods.
Environmental conservation is an area where AI can provide significant help for many types of tasks. Oil, plastic, anthropogenic noise, overfishing and global warming are known to affect marine ecosystems (flora, fauna) inducing a drastic decrease of marine biodiversity and ecosystem services. The assessment of marine animals’ distribution could benefit from automatic recognition of the presence of a species in a specific location. For this purpose, the passive acoustics monitoring can use underwater audio recordings and try to recognize the sound produced by the species. This work compares the performance of classical computer vision algorithms and modern deep learning methods for the task of identifying if a spectrogram contains the characteristic sound produced by the brown meagre. An accuracy of 95% was achieved using a deep convolutional neural network based on a recent architecture and partially pretrained, outperforming classical computer vision algorithms.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.