Method for solving multi-frame overlapping error of unmanned aerial vehicle ground object target identification

Abstract

The invention discloses a method for solving a multi-frame overlapping error of unmanned aerial vehicle ground object target identification. The method comprises the following steps: 1, acquiring aerial image data of a ground target; 2, expanding a training data set; 3, preprocessing training data; 4, improving a loss function of the YOLOV3 model and outputting a detection result; 5, training an improved YOLOV3 ground object detection model; and 6, performing online application of a ground feature detection model. In order to avoid the problem of illegal building identification coincidence, a YOLOV3 model loss function and detection result output are improved, so that the model is more suitable for illegal building identification on the ground, the problem of motion blur of images shot by an unmanned aerial vehicle is reduced, the adaptability and robustness of a system are enhanced, and the control precision of the system is improved.

Description

technical field [0001] The invention relates to the field of unmanned aerial vehicle detection, in particular to a method for solving the multi-frame overlapping error of unmanned aerial vehicle object recognition. Background technique [0002] Urban illegal buildings refer to buildings that violate the relevant laws and regulations of urban planning and land management, have not obtained the planning permission of construction land, or have changed the planning permission regulations of construction projects without authorization. Illegal buildings not only disrupt the order of urban planning and construction, illegally occupy public resources, affect the image of the city, and pose great safety hazards, but also damage the credibility of the government and affect the future development of the city. Therefore, how to discover and deal with illegal buildings in a timely, accurate and efficient manner has become an urgent problem for urban management departments. [0003] At...

AI Technical Summary

Problems solved by technology

Deep learning generally detects the target through the anchor frame. The anchor frame is generally pre-designed with parameters such as height and width, and then the network detects the target through the anchor frame. However, when multiple anchor frames detect the same target, the phenomenon of repeated target detection is prone to occur.

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