Detecting Objects in Aerial Photographs using Neural Network Techniques
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Abstract
This article discusses the study of aerial image analysis obtained from Unmanned Aerial Vehicles (UAVs) using the principles of artificial neural networks for image recognition. In this study, the author reviews the literature on image analysis for object detection and explores which models are suitable for the task of object detection in aerial images. The author selects the YOLO, Retinanet, and Fast R-CNN detection models after careful consideration. Through experimentation and study, the article reveals that when detecting objects in aerial images captured by UAVs, it is crucial to choose a detection model that aligns well with the equipment used for image capture. The experimental results show that utilizing the YOLO model yields a mean Average Precision (mAP) of up to 58.5% and a processing speed of 158.13 frames per second. These results highlight the superior accuracy and speed of object detection compared to other models tested with aerial images captured by unmanned aerial vehicles.
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