NATO Standard Mission Task Symbols Pose Detection Based on Symbol Requirements
Name
Karl-Kristjan Kõverik
Abstract
The objective of this master’s thesis is to develop a series of neural network models that can accurately detect the pose of NATO mission task symbols. This research is significant as NATO mission task symbols play a critical role in planning military operations and automatically identifying their identity, location and pose can save valuable time and resources when digitizing military plans.
The first step of research involved determining the essential components required to recreate mission task symbols into the KOLT digital planning system. This required a thorough analysis of the mission task symbols and their underlying characteristics. Through this analysis, we were able to identify the crucial features that the models must detect to sufficiently characterize each mission task symbol.
The second task of research involves developing models that can detect the rotations of the mission task symbols accurately. Additionally, for trajectory symbols, it is essential to develop models that can detect and describe the symbols’ trajectory accurately. However, detecting poses of hand-drawn symbols is challenging, as there is often significant variability among symbols with the same meaning. Therefore, the model must be designed to account for this variability.
The success of research will have significant implications for military operations. Accurate and efficient recognition of the poses of mission task symbols complements YOLO-based localization that is concurrently implemented in the research group, enabling AI-based digitalization of military plans. Fast digitalization enhances situational awareness and decision-making capabilities.
The first step of research involved determining the essential components required to recreate mission task symbols into the KOLT digital planning system. This required a thorough analysis of the mission task symbols and their underlying characteristics. Through this analysis, we were able to identify the crucial features that the models must detect to sufficiently characterize each mission task symbol.
The second task of research involves developing models that can detect the rotations of the mission task symbols accurately. Additionally, for trajectory symbols, it is essential to develop models that can detect and describe the symbols’ trajectory accurately. However, detecting poses of hand-drawn symbols is challenging, as there is often significant variability among symbols with the same meaning. Therefore, the model must be designed to account for this variability.
The success of research will have significant implications for military operations. Accurate and efficient recognition of the poses of mission task symbols complements YOLO-based localization that is concurrently implemented in the research group, enabling AI-based digitalization of military plans. Fast digitalization enhances situational awareness and decision-making capabilities.
Graduation Thesis language
English
Graduation Thesis type
Master - Data Science
Supervisor(s)
Ardi Tampuu
Defence year
2023