Enhancing Object Detection Algorithms by Synthetic Aerial Images

• Published in: 2023 31st Signal Processing and Communications Applications Conference (SIU) pp. 1 - 4
• Main Authors: YILMAZ, Can, MARAS, Bahri, YILMAZ, Gorkem, CEYLAN, Goksu, HAMAMCIOGLU, Onder, ARICA, Nafiz, ERTUZUN, Aysin Baytan
• Format: Conference Proceeding
• Language: English; Turkish
• Published: IEEE 05-07-2023
• Subjects: Annotations; Autonomous aerial vehicles; Bounding Box; Deep Convolutional Neural Network (DCNN); Detection Algorithm; Object detection; Occlusion; Optical flow; S-UAV Synthetic Dataset; Segmentation; Signal processing algorithms; Synthetic data; Transformers; Unmanned Air Vehicle (UAV)
• DOI: 10.1109/SIU59756.2023.10223740

In order to accurately perform object detection by deep convolutional neural networks (DCNN) in videos, ob­tained from unmanned aerial vehicles (UAVs), many example images of objects containing annotations such as ground truth class information, bounding box, optical flow, occlusion and segmentation are required. Due to the difficulties faced during annotation of scenarios, and due to the inadequacy of the scenario diversity resulting from environmental conditions, a dataset containing above mentioned ground truths has not been found in the literature. In this study, synthetic aerial images with various annotation information were created in different scenarios while composing virtual worlds, and enhancing object detection algorithms is aimed. Enhancement of detection results of DCNN based object detection algorithms, trained with the support of synthetic aerial images, on real-world aerial images significantly, was observed during the experiments, conducted.