![]() ![]() In addition, to overcome the variability of light and background mixing problems, an improved supervised learning method (ISL) is proposed. The performances of the proposed approaches are reviewed according to the F1 score values, and ODRP Faster R-CNN, YOLO V5, YOLO V6 and DETR approaches achieved F1 scores of 0.909, 0.956, 0.948 and 0.922, respectively. The F1 score metric is widely used to examine the performance of methods in deep learning models. In the proposed ODRP approach, the performances of convolutional neural network (CNN)-based Faster R-CNN, YOLO V5, YOLO V6 and transformer-based DETR models as deep learning models for object detection are examined. ![]() Finally, the spatial location of the detected object on the earth is calculated using distance, direction and GPS data with rotation and projection methods. Then, the object’s distance is calculated at the point where the photograph is taken. In the proposed approach, a deep learning method detects a street sign object in the EXIF. The performance of the proposed approach has been examined on the natural EXIF data sets obtained from the Kayseri Metropolitan Municipality. ![]() In this study, a new hybrid approach, ODPR, which utilizes object detection (O), distance estimation (D), rotation (R) and projection (P) methods, is proposed to detect street sign objects in EXIF with their locations. Transferring the objects in EXIF data sets with absolute coordinates on the earth significantly contributes to GIS. On the other hand, exchangeable image file (EXIF) format is a special file format that contains camera direction, date-time information and GPS location provided by a digital camera that captures the images. Thus, it is essential to collect and update these data. The most important raw material in GIS is spatial data. Geographical information systems (GIS) are the systems where spatial data are stored and analyzed. ![]()
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