Accuracy assessment of UAV-post-processing kinematic (PPK) and UAV-traditional (with ground control points) georeferencing methods.

The use of unmanned aerial vehicles (UAV) in photogrammetric mapping/surveying facilities has increased recently due to the developments on photogrammetric instruments and algorithms that enhance high-quality final products (orthoimages, digital surface model-DSM, etc.) in fast, accurate, and economical way. The aim of this study was to assess the accuracy of a UAV-based post-processing kinematic (PPK) solution. To do that, two methods were implemented with PPK solution and georeferencing with ground control points (GCPs). According to the statistical results, root mean square error (RMSE) values obtained from the GCPs and PPK solutions in the horizontal component are 6.5 cm and 5.4 cm, respectively. The RMSE values in the vertical component (ellipsoidal heights) were obtained as 4.8 cm (GCPs) and 5.2 cm (PPK), respectively. The results show that UAV-PPK method can also be used to produce photogrammetric products where high accuracy (≤ 10 cm) is required without GCPs. In addition, the results obtained regarding the use of this method clearly show that it can be applied in many different fields such as agriculture, forestry, natural disasters, and geomatics.

Deep neural network approaches for detecting gastric polyps in endoscopic images.

Gastrointestinal endoscopy is the primary method used for the diagnosis and treatment of gastric polyps. The early detection and removal of polyps is vitally important in preventing cancer development. Many studies indicate that a high workload can contribute to misdiagnosing gastric polyps, even for experienced physicians. In this study, we aimed to establish a deep learning-based computer-aided diagnosis system for automatic gastric polyp detection. A private gastric polyp dataset was generated for this purpose consisting of 2195 endoscopic images and 3031 polyp labels. Retrospective gastrointestinal endoscopy data from the Karadeniz Technical University, Farabi Hospital, were used in the study. YOLOv4, CenterNet, EfficientNet, Cross Stage ResNext50-SPP, YOLOv3, YOLOv3-SPP, Single Shot Detection, and Faster Regional CNN deep learning models were implemented and assessed to determine the most efficient model for precancerous gastric polyp detection. The dataset was split 70% and 30% for training and testing all the implemented models. YOLOv4 was determined to be the most accurate model, with an 87.95% mean average precision. We also evaluated all the deep learning models using a public gastric polyp dataset as the test data. The results show that YOLOv4 has significant potential applicability in detecting gastric polyps and can be used effectively in gastrointestinal CAD systems. Gastric Polyp Detection Process using Deep Learning with Private Dataset.