Abstract: A system for real-time target identification of an unmanned aerial vehicle (UAV) based on an embedded technique and an improved YOLO4 algorithm, and relates to the field of real-time target detection is disclosed. The system for real-time target identification of a UAV based on an embedded technique and an improved YOLO4 algorithm abandons a conventional “one-to-one” mode, and can be used for a plurality of UAV data processing applications, and multiple users in different geographical locations can further remotely manage and control the system. Particularly, in a target identification client of the present disclosure, to obtain a better real-time identification effect, a YOLOv4 target identification algorithm is improved, so that a better identification result and a faster identification speed are obtained.

Abstract: The present disclosure provides a method for detection of soil heavy metal pollution using an unmanned aerial vehicle (UAV) and an X-ray fluorescence (XRF) technology. Based. Based on hardware equipment such as the UAV, XRF analyzer, and embedded equipment, the present disclosure develops an altitude hold module of the system and a ground-contact monitoring module, and assists the UAV to achieve safe and accurate fixed-point hovering, and develops a driving device for data acquisition to replace manual control and realize the automatic acquisition of XRF data. The data inversion method is realized by using embedded equipment, and after the data is acquired by the portable XRF analyzer near the ground, the algorithm research of inversion processing of contents of heavy metal elements in soil is realized, such that the portable XRF analyzer can automatically and accurately detect the contents of heavy metals in soil at a certain distance.