Unmanned aerial vehicle tracking method based on deep learning image processing technology

Abstract

The invention discloses an unmanned aerial vehicle tracking method based on deep learning image processing technology, and belongs to the technical field of unmanned aerial vehicles. The tracking method comprises the following specific steps: (1) collecting a to-be-recognized target image; (2) preprocessing the image ; (3) recognizing a target; (4) fusion positioning the target; (5) measuring the speed of the target; and (6) performing tracking control. According to the method, a YOLOv3 algorithm model is used to carry out learning training on the target image, and image enhancement processing is carried out based on a CLAHE image enhancement preprocessing algorithm, so that the target recognition precision of an unmanned aerial vehicle in complex environments with environment occlusion or illumination condition change can be improved, and the situation that the unmanned aerial vehicle cannot recognize the target is avoided. In addition, a laser radar and a millimeter-wave radar are used to synchronously measure the position and the moving speed of the target in real time, and the Kalman filtering algorithm fusion is utilized to calculate the optimal solution, so that the positioning and tracking performance of the unmanned aerial vehicle in complex environments with heavy snow and heavy fog can be improved, and the target can be prevented from being lost.

Description

technical field [0001] The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle tracking method based on deep learning image processing technology. Background technique [0002] After retrieval, Chinese Patent No. CN108010080A discloses a UAV tracking system and method. Although the inventive method can guide the UAV to accurately track the target beacon through the infrared rays emitted by the infrared emitting device, it is easily affected by external factors. , it is impossible to track targets in complex environments, and the tracking performance is poor; unmanned aircraft, referred to as "unmanned aerial vehicle" ("UAV"), is an unmanned aircraft controlled by radio remote control equipment and its own program control device , UAV is actually a general term for unmanned aerial vehicles. From a technical point of view, it can be divided into: unmanned fixed-wing aircraft, unmanned vertical take-off and landing a...

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Problems solved by technology

[0002] After retrieval, Chinese Patent No. CN108010080A discloses a UAV tracking system and method. Although the inventive method can guide the UAV to accurately track the target beacon through the infrared rays emitted by the infrared emitting device, it is easily affected by external factors. , it is impossible to track targets in complex environments, and the tracking performance is poor; unmanned aircraft, referred to as "unmanned aerial vehicle" ("UAV"), is an unmanned aircraft controlled by radio remote control equipment and its own program control device , UAV is actually a general term for unmanned aerial vehicles. From a technical point of view, it can be divided into: unmanned fixed-wing aircraft, unmanned vertical take-off and landing aircraft, unmanned airship, unmanned helicopter, unmanned multi-rotor aircraft, unmanned Compared with manned aircraft, it has the advantages of small size, low cost, convenient use, low requirements for the combat environment, and strong battlefield survivability. In recent years, with the development of automation technology, computer vision technology, etc. With the continuous improvement of the level of science and technology, UAVs have been rapidly developed in military, industrial and civilian fields. As an important branch of UAV application technology, the target tracking technology of micro-UAVs is widely used in national public security fields such as explosion-proof and anti-terrorism. , traffic monitoring, disaster relief and other aspects have broad application prospects, and have attracted great attention from scholars from various countries, and have become one of the most active research directions in this field; however, most of the existing UAV tracking UAV positioning uses GPS technology, It is impossible to track targets in complex environments, and is easily affected by factors such as changes in lighting conditions, object occlusion, and snow and weather, resulting in tracking and shooting targets that cannot be recognized or lost. Therefore, an image processing technology based on deep learning has been invented. UAV tracking methods become particularly important;

[0003] Most of the existing UAV tracking methods use GPS technology or lidar to locate the target, and combine traditional target algorithms to identify and track, but these methods are vulnerable to environmental occlusion, and are easily affected by factors such as changes in weather and lighting conditions. , the tracking performance is poor, and it is easy to cause the target to be unrecognized or lost; therefore, we propose a UAV tracking method based on deep learning image processing technology

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