Object detection in aerial images (ODAI) is an important task in computer vision and has applications in several areas. Recently, researchers directed their efforts to the ODAI, which requires detectors capable of dealing with arbitrary orientations, large variations in aspect ratios, densely clustered objects, multiple classes and instances per image. Our work is focused on objects that are relevant to defense systems. Such defense-related objects may present special challenges for detection and a reliable detector may be very useful as information source for defense systems. We have used publicly available aerial images and implemented some detectors based on Rotation-equivariant Detector - ReDet, which presented a very good performance for a broad class of objects. We tested such detectors using only defense-related objects. Our tests included dataset with and without data augmentation. The results achieved are consistent with the results published in some previous competitions.
