Optimization of YOLOV7's hyper parameters for simultaneous object detection in satellite imagery

Abstract

Object detection is crucial for computer vision applications that use satellite imagery, such as precision agriculture, urban planning, and military applications.Recognizing objects in satellite images is challenging due to numerous factors, including the sheer number of objects, the variety in their positions, the range in their sizes, the quality of the lighting, and the existence of a dense background.Background complexity, differences in data capture geometry, geography, and illumination, and an abundance of different types of objects all contribute to making automatic detection in satellite images particularly difficult.There have been many advancements in object detection methods over time, including YOLO and its variations, CNN and its offshoots, DETR and its offshoots, and so on; nonetheless, it is still required to test these methods on the requisite data set to determine their true efficacy.Researchers have investigated the idea of autonomously detecting structures, automobiles, and other things to reduce the risk of human error and speed up the procedure.Improvements in deep learning algorithms and hardware systems have allowed us to accurately identify a broader range of objects in ultra-high-resolution satellite imagery.Through parameter adjustment and analysis of results on the Xview dataset, we determine the most effective technique for multiple item detection and compare it to other models.