OBJECT DETECTION IN SATELLITE IMAGES USING DEEP LEARNING

Abstract

The article focuses on assessing object detection performance in satellite images using the YOLOv4 network. As satellite image quantity and quality increase, intelligent observation methods become crucial. Deep learning, particularly Convolutional Neural Networks (CNNs), has excelled in computer vision, prompting exploration in remote sensing imagery. The study evaluates YOLOv4 effectiveness in detecting objects, conducting tests on DIOR and HRRSD datasets. YOLOv4 outperforms other CNN models in detection accuracy, showcasing its potential for efficient satellite image object detection. The evaluation involves data preprocessing, model training, and comprehensive analysis of results from both datasets. YOLOv4's strengths lie in diverse scenario handling and rapid learning, as identified through literature analysis. The study demonstrates YOLOv4's applicability and superiority in satellite image object detection, offering more accurate and efficient methods for remote sensing applications. The insights gained guide future studies and applications in remote sensing and computer vision, contributing to improved observation techniques in satellite imagery.