Visible light airport airplane detection method based on potential target points

Abstract

The invention discloses a novel visible light airport airplane detection method based on potential target points. The method mainly comprises the following steps: proposing a multi-contrast image segmentation method, calculating the position of a potential target point through a segmentation area of each segmentation image, and proposing a data structure similar to a set to complete the storage ofthe potential target points because the plurality of segmentation images are easy to generate redundant potential target points; secondly, counting the sizes of the aircraft targets, providing a self-adaptive method based on statistics to generate detection windows of different sizes, and matching the detection windows of different sizes with the sizes of the aircraft targets to a great extent; finally, training an aircraft target classifier in a classifier mode of combining Fourier Hog features and Adaboost; and taking the potential target point as a central point, intercepting a current region by detection windows with different sizes, and judging whether the current region contains the aircraft or not by means of a trained classifier, thereby completing the detection of the airport aircraft.

Description

technical field [0001] The present invention relates to the technical field of digital image processing, in particular to a method for detecting airport aircraft in remote sensing images, and more specifically to a new and efficient detection of airport aircraft targets under visible light based on potential target points and multi-scale detection windows method. Background technique [0002] Target detection mainly focuses on specific objects in the image, and obtains the category information and location information of the target through detection technology. Detection technology can be roughly divided into two categories: detection based on traditional image processing and machine learning algorithms and target detection based on deep learning. Based on traditional detection methods such as sliding window + classifier or target candidate frame + classifier, the former has the disadvantages of slow speed and low efficiency, while the latter has defects such as insufficien...

AI Technical Summary

Problems solved by technology

Although target detection technology has developed rapidly, there are still a series of challenges for airport aircraft detection in remote sensing images. For example, the actual algorithm operating environment has high requirements for hardware conditions, the background environment of the airport is too complex, and the shooting angle of remote sensing images is changeable. Uneven lighting, etc.

The complexity of these actual situations has led to the lack of a low-hardware requirement and high-efficiency airport aircraft detection algorithm under visible light.

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