Drone detection using deep learning from thermal and high resolution camera images

There is a significant increase in the activity of the threatening use of drones. In addition to these threatening uses, the situations they create are becoming a growing problem. Due to these situations, the determination of the flight activity that creates the drone activities and the determination of the negative situations in the detected flight activity will be a solution for the growing problem. For these detection and estimation studies, deep learning methods were used. Different from acoustic, thermal and radar studies, an image-assisted system study has been carried out in sensor-assisted detection systems in the literature. In this study, by considering the cases where the detection success has decreased in the literature studies and the detection is not done, the detection activity at a common point in the detection activity made with the same angle and timing from the thermal and high-resolution standard camera has been studied. For the success criteria of detection activities in the study, comparative studies were carried out in Faster - RCNN, YOLOv4, v5 and SSD ResNet deep learning methods with the data set collected in different weather conditions, background densities and complexities. It has been improved by the comparisons made on the application and the determination of the most successful method according to the image size and features. With the improved method, in case the thermal density of the detection objects is close to the background density, it is supported by a high-resolution standard camera, and in cases where the object has a similar RGB value background or a complex background, the standard camera is supported with a thermal camera to increase the detection success. In the detection process, which is provided with the support of these two systems and whose success is increased, a separate training has been carried out on the determined method for distinguishing objects that provide mobility in the air such as helicopters, airplanes and birds, which also pose a problem in the literature. Thanks to the study, the detection of drones, whose mobility and number is constantly increasing, has been made more successful.