Dynamical System Modeling and Stability Investigation�DSMSI-2023

Dedicated to the 77th anniversary of the outstanding Ukrainian scientist

professor Denys Khusainov

December 19-21, 2023, Kyiv, Ukraine

Software for UAV Images Processing for Object Identification

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Kateryna Merkulova, Yelyzaveta Zhabska and Ivan Ivanenko

Taras Shevchenko National University of Kyiv�

Introduction

With the growing use of unmanned aerial vehicles (UAVs) in fields ranging from military surveillance to geodesy and environmental monitoring, image processing research is becoming a necessary component for the effective use of these technologies. One of the most significant areas of research is the identification of vehicles through the analysis of images obtained from UAVs. This article explores the methods and technologies used to process UAV imagery to accurately identify different types of vehicles.

Knowledge and understanding of modern image processing methods for the purpose of vehicle identification is becoming increasingly important for maintaining safety, efficiency and sustainable development of society. The results of these studies depend not only on the development of the latest technologies, but also on providing our world with greater safety and efficiency in various areas of life.

Solving the problem of automating the process of detecting suspicious objects on UAV images during martial law in Ukraine is an important aspect of security and control in the state. That is why the developed software is designed to identify different types of vehicles on UAV images. The discussed topic is quite relevant at the moment, as drones have become an important tool in the process of waging war. And that is why operative identification of suspicious vehicles on UAV images is a very urgent task in today’s realities for our country.

Dynamical System Modeling and Stability Investigation, DSMSI-2023

Methods of research

ResNet model training process

Dynamical System Modeling and Stability Investigation, DSMSI-2023

MobileNet model training process

EfficientDet model training process

Methods of research

EfficientDet network performed best in the training process, as it has the lowest values of the loss functions. It should be noted that the value of classification_loss in all three models is almost at the same level, while localization_loss has a much smaller value in the EfficientDet model.

Dynamical System Modeling and Stability Investigation, DSMSI-2023

Results of model training

Methods of research

Dynamical System Modeling and Stability Investigation, DSMSI-2023

Research results

Quality criterions for object identification methods

Dynamical System Modeling and Stability Investigation, DSMSI-2023

Graphs of the IoU(N) function for three identification methods

Research results

Graphs of the R(N) function for three identification methods

Dynamical System Modeling and Stability Investigation, DSMSI-2023

Graphs of the function T(N) for three identification methods

Conclusion

Having evaluated the results of the quality criteria for each of the researched identification methods, while taking into account the priorities of the quality criteria, it was concluded that the method based on the MobileNet model is the most optimal among the researched methods in the context of vehicle identification on UAV images. Because it showed the best results for the quality criterion R, which has the highest priority, while breaking away from the other two methods by a margin (by 11.3% - ResNet; by 48% - EfficientDet). The next most important is the IoU quality criterion, according to which the method based on the MobileNet model showed the second result, lagging behind the ResNet by 1.3%. In other words, MobileNet and ResNet are actually equal to each other according to this criterion. According to the third least significant quality criterion, the MobileNet-based method showed again the second result, which is three times worse than the first result, which was shown by the EfficientDet-based method. Again, it may seem that this is quite a noticeable difference, but if we take into account the fact that the obtained speed values are of the order of 10-3 seconds, then in practice this difference will not be noticeable. Although EfficientDet is the fastest in the context of vehicle identification in UAV images, on the other hand it performed the worst in two other more significant quality criteria. Therefore, based on the obtained results, the method based on the EfficientDet model is the worst in the context of vehicle identification in UAV images according to the given quality criteria, taking into account their priority.

Dynamical System Modeling and Stability Investigation, DSMSI-2023

Thank you for your attention!