Comparison of Object Detection Models using Convolutional Neural Networks in Aerial Image from Unmanned Aerial Vehicles
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Abstract
This research article studies and compares various models used for object detection in aerial imagery captured by Unmanned Aerial Vehicle (UAV). Two types of objects are detected: buildings and vehicles. Machine learning models are used for object detection, and various models are compared to identify their advantages and disadvantages. The following models are compared: Faster R-CNN, MobileNetv1, Retinanet50, YOLOV4, YOLOV4-tiny, YOLOv7, and EfficientDet. The experiments found that YOLOV7 achieved the highest detection accuracy of 58.5%, outperforming MobileNetv1, YOLOV4, Faster R-CNN, YOLOV-tiny, EfficientDet, and Retinanet50, which achieved accuracies of 49.5%, 45.1%, 21.2%, 17.6%, 14.5%, and 1.2%, respectively. The model with the highest speed was MobileNetv1, which achieved a speed of 196.01 frames per second. This accuracy and speed are sufficient for object detection tasks in aerial image from Unmanned Aerial Vehicle.
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