Simulation and real-life implementation of UAV autonomous landing system based on object recognition and tracking for safe landing in uncertain environments.

The use of autonomous Unmanned Aerial Vehicles (UAVs) has been increasing, and the autonomy of these systems and their capabilities in dealing with uncertainties is crucial. Autonomous landing is pivotal for the success of an autonomous mission of UAVs. This paper presents an autonomous landing system for quadrotor UAVs with the ability to perform smooth landing even in undesirable conditions like obstruction by obstacles in and around the designated landing area and inability to identify or the absence of a visual marker establishing the designated landing area.

Anomaly Detection in Time Series Data Using Reversible Instance Normalized Anomaly Transformer.

Anomalies are infrequent in nature, but detecting these anomalies could be crucial for the proper functioning of any system. The rarity of anomalies could be a challenge for their detection as detection models are required to depend on the relations of the datapoints with their adjacent datapoints. In this work, we use the rarity of anomalies to detect them.

YOLOv5 with ConvMixer Prediction Heads for Precise Object Detection in Drone Imagery.

The potency of object detection techniques using Unmanned Aerial Vehicles (UAVs) is unprecedented due to their mobility. This potency has stimulated the use of UAVs with object detection functionality in numerous crucial real-life applications. Additionally, more efficient and accurate object detection techniques are being researched and developed for usage in UAV applications.