Dr. Ammar Abdul Shahid Abdul Hamid and Dr. Adi Bashir Issa, lecturers at the University of Basra, College of Engineering, Department of Electrical Engineering, published a joint scientific research in cooperation with professors from the University of Basra and the University of Sussex Brighton in the United Kingdom in the Tikrit Journal of Engineering Sciences entitled Aircraft Detection Using Deep Learning Based on SVM and VGG. The research included a proposal for an algorithm to recognize aircraft regardless of different categories such as aircraft model, size and color. The main challenges in automatic aircraft detection tasks can be represented by differences in the shape of aircraft in addition to their direction and the amount of similarity with other objects. Therefore, an aircraft detection system needs to be designed so that it can be effectively distinguished without the influence of a set of characteristics such as rotation, different shapes and models, accuracy, type, and color. The system designed to detect aircraft consists of three main stages: feature extraction stage, aircraft detection and evaluation of detection accuracy. To extract features, deep learning technology (VGG) was used to find the accurate features. While aircraft were detected using the machine learning algorithm (SVM). For the purpose of evaluating the designed system, two datasets were used, Caltech-101 and FGVC-Aircraft, where the results using F1 score showed 99% for Caltech-101 dataset and 98% for FGVC-Aircraft dataset.
