Bilge Han Taş, M.Coşkun Irmak, M.Turan, S.Haşıloğlu, A.2025-05-102025-05-102021978166542908510.1109/UBMK52708.2021.95589072-s2.0-85125835237https://doi.org/10.1109/UBMK52708.2021.9558907https://hdl.handle.net/20.500.14720/2932The study was carried out in order to enable systems witli weak processing power and motion to detect objects using cloud services. In addition, the dataset is expanded by continuous labeling to create big data. In the study, it is aimed to detect objects using cloud-based deep learning methods with an unmanned aerial vehicle (UAV). In the study, training processes were carried out with Google Colaboratory, a cloud service provider. The training processes are a YOLO-based system, and a convolutional neural network was created by revising the parameters in line with the needs. The convolutional neural network model provides communication between neurons in the convolutional layers by bringing the image data to the desired pixel ranges. Unlabeled pictures are included in the training by being tagged. In this way, it is possible to continuously enlarge the data pool. Since the microcomputers used in UAVs are insufficient for these processes, a cloud-based training model has been created. As a result of the study, cloud-based deep learning models work as desired. It is possible to show the accuracy of the model with the low losses seen in the loss functions and the mAP value. Graphic cards with high processing power are needed to provide training. It is essential to use powerful graphics cards when working on image data. Cost reduced by using cloud services. The training was accelerated and high-rate object detections were made. YOLOv5x was used in the study. It is preferred because of its fast training and high frame rate. Recall 80% Precision 93% mAP 82.6% values were taken. © 2021 IEEEeninfo:eu-repo/semantics/closedAccessCloud Based Deep LearningConvolutional Neural NetworkPuddle DetectionReal Time Object DetectionUnmanned Aerial VehicleYolov5Conference ObjectN/AN/A598602