Unmanned aerial vehicle infrared target real-time detection method

A real-time detection and target detection technology, applied in neural learning methods, computer parts, instruments, etc., can solve the problems of lack of research and practice in target detection methods, and achieve the goal of improving detection capabilities, high-precision target detection, and reducing computational complexity. Effect

Pending Publication Date: 2020-07-24
CHENGDU UNIVERSITY OF TECHNOLOGY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to: provide a real-time detection method for infrared targets of unmanned aerial vehicles, to solve the problem that the above-mentioned existing large...

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  • Unmanned aerial vehicle infrared target real-time detection method
  • Unmanned aerial vehicle infrared target real-time detection method
  • Unmanned aerial vehicle infrared target real-time detection method

Examples

Experimental program
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Embodiment 1

[0042] Such as figure 1 , a method for real-time detection of an infrared target of an unmanned aerial vehicle, comprising the steps of:

[0043] (1) Build IT-YOLO UAV infrared target detection network;

[0044] (2) Collect and make UAV infrared target detection data set;

[0045] (3) Training and generating a real-time infrared target detection model for drones;

[0046] (4) UAV detects infrared targets.

[0047] In the technical solution of this application, on the basis of the Tiny-YOLOV3 lightweight target detection network, the backbone feature extraction network is improved and the network detection layer is replaced, and the IT-YOLO UAV infrared target detection network is constructed. Collect and produce UAV infrared target detection data sets under human-machine vision, train and generate UAV real-time infrared target detection models, which are applied to visible light cameras and ordinary digital night vision of UAVs at night when there is no light, rain and fog,...

Embodiment 2

[0049] as attached Figure 1-4 , on the basis of Embodiment 1, the IT-YOLO UAV infrared target detection network in step (1) is to improve the backbone network and detection network on the basis of the Tiny-YOLOV3 lightweight target detection network. The IT-YOLO UAV infrared target detection network adopts the basic structure of Tiny-YOLOV3. According to the characteristics of infrared images, it extracts shallow convolutional layer features to improve the detection ability of small infrared targets. It uses a single-channel convolution kernel to reduce the amount of calculation and detect Part of the detection method based on the CenterNet structure is used to reduce the false detection rate and improve the detection speed.

[0050] For the feature extraction of long-distance low-resolution infrared small targets, the shallow convolution Conv4 layer of the Tiny-YOLOV3 target detection network can more effectively represent the semantic information of infrared small targets, ...

Embodiment 3

[0056] as attached Figure 1-5 , on the basis of Example 1, in the night environment, the infrared thermal imaging platform equipped with the UAV is used to shoot at low altitude and high altitude respectively, and the infrared images that are clear and available for identification and detection are collected from the top-down perspective, and 8000 infrared images are collected. Infrared thermal imaging images of night scenes from the perspective of UAVs are used as UAV infrared target detection datasets.

[0057] Divide the UAV infrared target detection data set into training set and test set according to the ratio of 5:1, use the YOLO-MARK tool to mark the two types of targets to be detected, pedestrians and vehicles; in the model training process, the images in the data set All samples are converted into images of 416×416 pixels. During training, 100 images are used as a batch for small batch training. A batch of images are trained, and the weights are updated once. The dec...

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Abstract

The invention discloses an unmanned aerial vehicle infrared target real-time detection method, which belongs to the field of unmanned aerial vehicle real-time monitoring, and comprises the following steps of: (1) constructing an IT-YOLO unmanned aerial vehicle infrared target detection network; (2) acquiring and manufacturing an infrared target detection data set of the unmanned aerial vehicle; (3) training to generate an unmanned aerial vehicle real-time infrared target detection model; and (4) detecting an infrared target by the unmanned aerial vehicle. On the basis of a Tiny-YOLOV3 lightweight target detection network, improvement of a trunk feature extraction network and replacement of a network detection layer are carried out; an IT-YOLO unmanned aerial vehicle infrared target detection network is constructed; an unmanned aerial vehicle infrared target detection data set is collected and manufactured under the vision of the unmanned aerial vehicle by using infrared imaging; and anunmanned aerial vehicle real-time infrared target detection model is trained and generated. The method is applied to an unmanned aerial vehicle to detect a target in a detection environment which cannot be dealt with by a visible light camera and a common digital night vision device such as a night lightless weather influence environment and a rainy and foggy weather influence environment, clearpedestrian and vehicle infrared spectral images can be collected at a distance of about 100 meters, and real-time and high-precision target detection is carried out.

Description

technical field [0001] The invention belongs to the technical field of real-time monitoring of unmanned aerial vehicles, and in particular relates to a method for real-time detection of infrared targets of unmanned aerial vehicles. Background technique [0002] Today, the military application of drones has become more mature and in-depth. With the rapid development of technologies such as flight control, communication, positioning and navigation, more application fields and values ​​of drones have been discovered by people, such as film and television aerial photography, entertainment selfie, agricultural plant protection, traffic management and so on. However, in the dark environment at night and under harsh weather conditions, drones need to detect targets at a long distance with high precision, and relying on visible light illumination or low-light night vision equipment has great limitations. [0003] The imaging principle of the infrared thermal imaging system is infra...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/08G06V20/194G06V20/13G06V2201/07G06N3/045
Inventor 易诗谢家海
Owner CHENGDU UNIVERSITY OF TECHNOLOGY
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