A real-time detection method for UAVs in dynamic environments

Abstract

The invention discloses an unmanned aerial vehicle real-time detection method used in a dynamic environment. The unmanned aerial vehicle real-time detection method comprises the steps of 1, establishing an Otsu-HSV color model; Step 2, inputting an image into the Otsu-HSV color model; using a BING algorithm to detect, and combining an overlapping area formula to eliminate a part of detection windows with large coincidence degrees; and step 3, combining an NMS algorithm of a linear weighting method, and replacing the original confidence score of some windows needing to be suppressed with relatively low confidence by using the NMS algorithm. According to the method, the HSV space and the maximum between-cluster variance method are combined, the overlapping area and the linear weighted scoreare introduced, the improved BING / NMS algorithm is provided, and finally, on the premise that the real-time performance is met, the detection accuracy and the optimal overlapping rate of the unmannedaerial vehicle are ensured, and the false detection rate and the missing detection rate are reduced.

Description

technical field

[0001] The invention relates to an unmanned aerial vehicle detection method, in particular to a real-time detection method for an unmanned aerial vehicle in a dynamic environment. Background technique

[0002] In modern air combat, drones have played an increasingly important role due to their small size, low cost, and high safety. Among them, the detection and recognition of enemy targets has always been a research hotspot in the industry. With the continuous development of aerospace technology, the battlefield environment has higher and higher requirements for real-time performance and accuracy. The method of manual interpretation of aerial image detection can no longer adapt to modern The needs of information warfare. If objects could be detected and recognized automatically, then truly autonomous drone flight would be feasible.

[0003] Target detection based on RGB color space, when color separation is performed, it is prone to mis-separation, missing ...

Examples

Embodiment 1

[0047] This embodiment provides a real-time detection method for unmanned aerial vehicles in a dynamic environment, the process is as follows figure 1 As shown, the following steps are included.

[0048] Step 1: Establish the Otsu-HSV color model, the specific operation is as follows:

[0049] 1. Convert the image from RGB color space to HSV color space;

[0050] 2. Extract the three channel images of H, S, and V, mainly use the V channel information, and use the maximum inter-class variance method for pre-segmentation;

[0051] 3. Further integrate the S channel information to form a new image, and perform a second segmentation on the new image to complete the second segmentation process to form the Otsu-HSV color model.

[0052] The traditional RGB space is composed of three components: red, green, and blue. There is a lot of unnecessary information between each component, which increases the amount of calculation; when performing color separation, it is easy to cause fal...

Embodiment 2

[0091] The algorithm of the present invention is compared with the UAV detection performance in the Otsu-HSV color model and the GRAY, RGB and HSV color spaces respectively. As shown in Table 1, the average accuracy rate and the best overlap rate of the Otsu-HSV color model are in the four colors It is the highest in the space, and its values ​​are 97.15% and 67.25% respectively. This is because when the gray level overlaps between the target and the background, the maximum inter-class variance method is used in the HSV color space to solve the gray level change trend in the image. Similar to the problem that the image information cannot be effectively reflected, and the UAV features extracted by the Otsu-HSV color model are more obvious, and the color information is less lost. Compared with other color spaces, it is more conducive to the subsequent detection of UAV targets in the present invention .

[0092] Table 1 Quantitative comparison of the algorithm of the present inve...

technical field

[0001] The invention relates to an unmanned aerial vehicle detection method, in particular to a real-time detection method for an unmanned aerial vehicle in a dynamic environment. Background technique

[0002] In modern air combat, drones have played an increasingly important role due to their small size, low cost, and high safety. Among them, the detection and recognition of enemy targets has always been a research hotspot in the industry. With the continuous development of aerospace technology, the battlefield environment has higher and higher requirements for real-time performance and accuracy. The method of manual interpretation of aerial image detection can no longer adapt to modern The needs of information warfare. If objects could be detected and recognized automatically, then truly autonomous drone flight would be feasible.

[0003] Target detection based on RGB color space, when color separation is performed, it is prone to mis-separation, missing ...

Examples

Embodiment 1

[0047] This embodiment provides a real-time detection method for unmanned aerial vehicles in a dynamic environment, the process is as follows figure 1 As shown, the following steps are included.

[0048] Step 1: Establish the Otsu-HSV color model, the specific operation is as follows:

[0049] 1. Convert the image from RGB color space to HSV color space;

[0050] 2. Extract the three channel images of H, S, and V, mainly use the V channel information, and use the maximum inter-class variance method for pre-segmentation;

[0051] 3. Further integrate the S channel information to form a new image, and perform a second segmentation on the new image to complete the second segmentation process to form the Otsu-HSV color model.

[0052] The traditional RGB space is composed of three components: red, green, and blue. There is a lot of unnecessary information between each component, which increases the amount of calculation; when performing color separation, it is easy to cause fal...

Embodiment 2

[0091] The algorithm of the present invention is compared with the UAV detection performance in the Otsu-HSV color model and the GRAY, RGB and HSV color spaces respectively. As shown in Table 1, the average accuracy rate and the best overlap rate of the Otsu-HSV color model are in the four colors It is the highest in the space, and its values ​​are 97.15% and 67.25% respectively. This is because when the gray level overlaps between the target and the background, the maximum inter-class variance method is used in the HSV color space to solve the gray level change trend in the image. Similar to the problem that the image information cannot be effectively reflected, and the UAV features extracted by the Otsu-HSV color model are more obvious, and the color information is less lost. Compared with other color spaces, it is more conducive to the subsequent detection of UAV targets in the present invention .

[0092] Table 1 Quantitative comparison of the algorithm of the present inve...

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