The importance and application of systems that work without human involvement are growing in many industries, particularly in the defense industry. Although minimizing the human role in these systems saves money and time, it also introduces new challenges if the accuracy rate in image processing systems isn’t high enough. Erroneous traffic sign detection for land vehicles running without human assistance, as well as a failure to correctly estimate the landing site in aircraft. In this study, images from the plane were collected and used to enhance object detection in autonomous systems. On these photos, several data augmentation approaches have been used to try to alleviate the detection challenges by increasing the object detection rate. Data reproduction methods included Gaussian, snow, shading, high gamma, Contrast Limited Adaptive Histogram Equalization (CLAHE), raised luminance, and lowered luminance methods, as well as data augmentation. The YOLOv4 algorithm was utilized to train the model for object detection. When the findings were examined, it was discovered that after 8000 repetitions, the success rate had increased to 94 percent, and the loss value had decreased to 0.42. In addition to the simulation environment, the trained model was also evaluated in a real ground vehicle.
Prepared by Firat Bozkaya, Abdullah Yusefi, Sukrucan Tiglioglu, Ahmet Kagan Kaya, Okan Kazanci, Mustafa Yasin Akmaz, Akif Durdu, Cemil Sungur