Application of artificial intelligence for target detection and tracking on unmanned aerial systems

Abstract

Ovaj se rad bavi analizom suvremenih algoritama za detekciju i praćenje objekata u slikama i videozapisima, s posebnim naglaskom na njihovu primjenu u zračnim besposadnim sustavima (ZBS, eng. Unmanned Aerial Systems – UAS). Cilj je prikazati prednosti i nedostatke najvažnijih pristupa temeljenih na dubokom učenju (eng. Deep Learning), koji omogućuju učinkovitu obradu podataka u stvarnom vremenu, što je od ključnog značaja za autonomno upravljanje i navigaciju zračnih besposadnih sustava. Analizirane su metode kao što su Faster R-CNN (Faster Region Based Convolutional Neural Network) i YOLO (You Only Look Once) za detekciju objekata, te algoritmi za praćenje poput MOSSE (Minimum Output Sum of Squared Error) i SORT (Simple Online and Realtime Tracking). Rezultati pokazuju da jednofazni algoritmi poput YOLO omogućuju brzu i dovoljno preciznu detekciju pogodnu za primjenu u realnom vremenu na platformama s ograničenim računalnim resursima, kakvi su tipični za zračne besposadne sustave. Korelacijski filtri poput MOSSE algoritma pokazali su se izuzetno učinkoviti za jednostavno i brzo praćenje objekata tijekom leta, čime omogućuju stabilno praćenje ciljeva unatoč promjenama u osvjetljenju i pozadini. U sklopu rada implementirani su i testirani odabrani algoritmi za detekciju i praćenje ciljeva korištenjem programskog okruženja Visual Studio Code, uz prethodnu instalaciju svih potrebnih alata i biblioteka za strojno učenje i računalni vid (eng. Computer Vision). Također su korišteni i online alati, poput RoboFlow-a, koji su značajno olakšali proces anotiranja slika i pripremu skupova podataka za treniranje modela. Primjena ovih tehnologija u zračnim besposadnim sustavima otvara širok spektar mogućnosti, uključujući nadzor iz zraka, traženje i spašavanje, inspekciju infrastrukture te mnoge druge domene gdje je autonomna analiza slike ključna za sigurnost i učinkovitost sustava. Korištenjem umjetne inteligencije omogućuje se zračnim besposadnim sustavima da autonomno prepoznaju, klasificiraju i prate različite objekte u složenim i dinamičnim okruženjima, čime se smanjuje potreba za stalnim ljudskim nadzorom. Osim toga, napredni algoritmi omogućuju prilagodbu sustava promjenjivim uvjetima leta, što povećava pouzdanost i učinkovitost misija, posebno u zahtjevnim scenarijima kao što su nadzor velikih područja, praćenje pokretnih ciljeva ili pomoć u hitnim intervencijama.This paper analyzes modern algorithms for object detection and tracking in images and videos, with a special emphasis on their application in unmanned aerial systems (UAS). The objective is to present the advantages and disadvantages of the most important approaches based on deep learning, which enable efficient real-time data processing, a key requirement for autonomous control and navigation of unmanned aerial systems. Methods such as Faster R-CNN (Faster Region-Based Convolutional Neural Network) and YOLO (You Only Look Once) for object detection, as well as tracking algorithms such as MOSSE (Minimum Output Sum of Squared Error) and SORT (Simple Online and Realtime Tracking), have been analyzed. The results show that single-stage algorithms such as YOLO provide fast and sufficiently accurate detection suitable for real-time applications on platforms with limited computational resources, which is typical for unmanned aerial systems. Correlation filters such as the MOSSE algorithm have proven to be highly effective for simple and rapid object tracking during flight, enabling stable target tracking despite changes in lighting and background. As part of the work, selected algorithms for object detection and tracking were implemented and tested using the Visual Studio Code development environment, following the installation of all necessary tools and libraries for machine learning and computer vision. Online tools such as RoboFlow were also used, significantly simplifying the image annotation process and dataset preparation for model training. The application of these technologies in unmanned aerial systems opens up a wide range of possibilities, including aerial surveillance, search and rescue, infrastructure inspection, and many other fields where autonomous image analysis is crucial for system safety and efficiency. By employing artificial intelligence, unmanned aerial systems can autonomously detect, classify, and track various objects in complex and dynamic environments, reducing the need for constant human supervision. Additionally, advanced algorithms enable the system to adapt to changing flight conditions, increasing the reliability and efficiency of missions, particularly in demanding scenarios such as large-area monitoring, moving target tracking, or emergency response operations