Automatic flying target detection and tracking in video sequences acquired from a camera mounted on another Unmanned Aerial Vehicle (UAV) is a challenging task due to the presence of non-stationary cameras in the system, dynamic motion of the moving target, and high-cost computation for real-time applications. In this paper, our aim is to automatically detect and track moving UAV by another one while simultaneously flying in the air. In order to provide efficiently in real-time applications, we develop a vision-based low-cost hardware system integrated with an independent ground control station. We initially created a new public dataset called ATAUAV that includes different types of UAV images obtained from videos recording in our experiments and searches on Google Images for the training process. Deep learning-based YOLOv3-Tiny (You Only Look Once) is used for target detection with the highest accuracy and fastest results. Kernelized Correlation Filter (KCF) adapted with YOLO, which runs on low-cost hardware, is used for real-time detected target tracking. We compared the performance of the proposed approach with different tracking algorithms. Experimental results show that the proposed approach provides the highest accuracy rate as 82.7% and a mean fps speed as 29.6 on CPU. The dataset can be downloaded at http://cogvi.atauni.edu.tr/ResearchLab/PageDetail/Our-ATAUAVs-Dataset-86 .

In diagnosing kidney stone disease, clinical specialists often apply medical imaging techniques such as CT, X‐Ray and US. Among these imaging techniques, X‐Ray is frequently chosen as the primary examination method in emergency services due to its low cost, accessibility, and low radiation levels. However, interpreting the X‐Ray images by inexperienced specialists can be challenging due to the low image quality and the presence of noise. In this study, we propose a computer‐aided diagnosis system based on deep neural networks to assist clinical specialists in detecting kidney stones using Direct Urinary System X‐Ray (DUSX) images. Firstly, in consultation with clinical specialists, we created a new dataset composed of 630 DUSX images and presented it publicly. We also defined preprocessing steps that incorporate image enhancement techniques such as GF, LoG, BF, HE, CLAHE, and CBC to enable deep neural networks to perceive the images more clearly. With these techniques, we considered the noise reduction in the DUSX images and enhanced the poor quality, especially in terms of contrast. For each preprocessing step, we created models to detect kidney stones using YOLOv4 and Mask R‐CNN architectures, which are common CNN‐based object detectors. We examined the effects of the preprocessing steps on these models. To the best of our knowledge, the combination of BF and CLAHE which is called CBC in this study, has not been applied before in the literature to enhance DUSX images. In addition, this study is the first in its field in which the YOLOv4 and Mask R‐CNN architectures have been used for the detection of kidney stones. The experimental results demonstrated the most accurate method is the YOLOv4 model, which includes the CBC preprocessing step, as the result model. This model shows that the accuracy rate, precision, recall, and F1‐score were found as 96.1%, 99.3% 96.5%, and 97.9% respectively in the test set. According to these performance metrics, we expect that the proposed model will help to reduce the unnecessary radiation exposure and associated medical costs that come with CT scans.

Streamflow forecasting holds a vital role in planning, design, and management of basin water resources. Accurate streamflow forecast provides a more efficient design of water resources systems technically and economically. In this study, various machine learning algorithms were used to model monthly streamflow data in the Coruh river basin, Turkey. For modeling, Support Vector Machines (SVM), Adaptive Boosting (AdaBoost), K-Nearest Neighbours (KNN) and Random Forest algorithms were considered and compared. Based on the test scores of the considered models with the hyperparameters, Random Forest based model outperforms all other models.