Unmanned aerial vehicle (UAV) is an expanding interest in numerous industries for various applications. Increasing development of UAVs is happening worldwide, where various sensor attachments and functions are being added. The multi-function UAV can be used within areas where they have not been managed before. Because of their accessibility, cheap purchase, and easy-to-use, they replace expensive systems such as helicopters- and airplane-surveillance. UAV are also being applied into surveillance, combing object detection to video-surveillance and mobility to finding an object from the air without interfering with vehicles or humans ground. In this thesis, we solve the problem of using UAV on autonomous sites, finding an object and critical situation, support autonomous site operators with an extra safety layer from UAVs camera. After finding an object on such a site, uses GPS-coordinates from the UAV to see and place the detected object on the site onto a gridmap, leaving a coordinate-map to the operator to see where the objects are and see if the critical situation can occur. Directly under the object detection, reporting critical situations can be done because of safety-distance-circle leaving warnings if objects come to close to each other. However, the system itself only supports the operator with extra safety and warnings, leaving the operator with the choice of pressing emergency stop or not. Object detection uses You only look once (YOLO) as main object detection Neural Network (NN), mixed with edge-detection for gaining accuracy during bird-eye-views and motion-detection for supporting finding all object that is moving on-site, even if UAV cannot find all the objects on site. Result proofs that the UAV-surveillance on autonomous site is an excellent way to add extra safety on-site if the operator is out of focus or finding objects on-site before startup since the operator can fly the UAV around the site, leaving an extra-safety-layer of finding humans on-site before startup. Also, moving the UAV to a specific position, where extra safety is needed, informing the operator to limit autonomous vehicles speed around that area because of humans operation on site. The use of single object detection limits the effects but gathered object detection methods lead to a promising result while printing those objects onto a global positions system (GPS) map has proposed a new field to study. It leaves the operator with a viewable interface outside of object detection libraries.

This project aims to contribute to the discussion regarding reproducibility of machinelearning research. This is done through utilizing the methods specified in the report ImprovingReproducibility in Machine Learning Research [30] to select an appropriateobject detection machine learning research paper for reproduction. Furthermore, this reportwill explain fundamental concepts of object detection. The chosen machine learningresearch paper, You Only Look Once (YOLO) [40] is then explained, implemented andtrained with various hyperparameters and pre-processing steps.While the reproduction did not achieve the results presented by the original machinelearning paper, some key insights were established. Firstly, the results of the projectdemonstrates the importance of pretraining. Secondly, the checklist provided by the NeurIPS[30] should be adjusted such that it is applicable in more situations.

<p dir="ltr">Unmanned Aerial Vehicles (UAVs) are increasingly applied in agricultural tasks such as crop monitoring, especially with AI-driven enhancements significantly increasing their autonomy and ability to execute complex operations without human interventions. However, existing UAV systems lack efficiency, intuitive user interfaces using natural language processing for command input, and robust security which is essential for real-time operations in dynamic environments. In this paper, we propose a novel solution to create a secure, efficient, and user-friendly interface for UAV control by integrating Large Language Model (LLM) with the case study on agricultural environment. In particular, we designed a four-stage approach that allows only authorized user to issue voice commands to the UAV. The command is issued to the LLM controller processed by LLM using API and generates UAV control code. Additionally, we focus on optimizing UAV battery life and enhancing scene interpretation of the environment. We evaluate our approach using AirSim and an agricultural setting built in Unreal Engine, testing under various conditions, including variable weather and wind factors. Our experimental results confirm our method's effectiveness, demonstrating improved operational efficiency and adaptability in diverse agricultural scenarios.</p>