Research on the Application of Drone Remote Sensing and Target Detection in Civilian Fields

Unmanned aerial vehicle (UAV) remote sensing has evolved as an efficient means of geospatial data acquisition. This paper provides a comprehensive review of the key technologies enabling intelligent analysis of UAV remote sensing images, specifically payload technology and object detection algorithms. Main payload systems including optical, multispectral, hyper-spectral, SAR, and LiDAR are introduced, with analyses of their working principles, performance benchmarks, and suitable application scenarios. In terms of object detection, traditional methods and emerging deep learning-based techniques are discussed and compared. The superior capabilities of anchor-free and Transformer-based detectors are analyzed through representative algorithms like CenterNet, FCOS, and DETR. Additionally, case studies demonstrate the broad applications and profound value of UAV remote sensing with intelligent target detection in areas such as precision agriculture, environmental conservation, infrastructure monitoring, and emergency response. Overall, this paper offers an in-depth examination of the landscape and trends of core technologies augmenting UAV remote sensing, providing guidance for future research and applications.