Today, the defense industry is utilizing computer vision solutions, particularly in autonomous weapons and unmanned aerial vehicles, due to advancements in technology. In recent years, technological capabilities such as autonomous target tracking and target recognition have provided great advantages to armies, soldiers, or, in short, the defense industry. Although it is difficult to access or research information about projects related to the defense industry, we can see that these definitions are made through research in the field of computer vision and libraries that use the CNN algorithm, such as YOLO, in target tracking and identification.
HaarCascade is a different object recognition algorithm than YOLO and has lower reliability and accuracy than YOLO, but it can still be considered as a second option. Many projects use HaarCascade only for capturing objects. For identifying the captured object, it is not very reliable. This is because the algorithm used in YOLO and similar CNN libraries is complex. These complex algorithms offer a high accuracy rate. Furthermore, the devices where these algorithms will be used require high GPU performance. For this reason, both algorithms offer different advantages depending on the area of use. In addition, one of the most important factors in defense industry projects is a high reliability rate. For this reason, it is known that the algorithms used in the field of computer vision in the defense industry are mostly CNN-based.
Examples of Computer Vision Usage Areas in the Defense Industry:
If we talk about the contributions of algorithms used in the defense industry to military operations, Spot, the robot of the US-based artificial intelligence and robotics company Boston Dynamics, recently participated in an operation with the French special operations team and successfully completed many tasks in the operation. During the operation, five more different robots from the Saint-Cyr Military Academy completed their testing process. These special robots, which use artificial intelligence, make many inferences from their environment through movements such as moving forward and stopping, and they largely use computer vision techniques when making these inferences.
Another area where computer vision is used in the defense industry is F-16 jets and unmanned aerial vehicles. To give examples of artificial intelligence algorithms for target tracking, situation-weather analysis, and runway capture, the altitude sensor, weather sensors, and GPS on unmanned aerial vehicles do not provide accurate results in situations that cause low visibility, such as rain and storm clouds. In addition, in cases such as sudden signal loss and disconnection, unmanned aerial vehicles must perform their duties by making their own decisions based on their location. The algorithms used in such cases can create an accurate decision structure by using real-time images taken from sensors and cameras using their own decision structures, computer vision techniques, and calculating many variables. Computer vision can be used not only as the main subject but also as supporting software to calculate the reliability rates of the sensors used.