Unmanned aerial vehicle weak and small target detection method based on space-time attention mechanism

A weak target and detection method technology, applied in the field of drones, can solve problems such as limited detection effect

Pending Publication Date: 2020-10-27
NAT UNIV OF DEFENSE TECH
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Problems solved by technology

[0005] (2) The existing target detection algorithms for head-up / short-range / regular size have limited effect on the detection of long-range / small-size targets taken from the perspective of drones;

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  • Unmanned aerial vehicle weak and small target detection method based on space-time attention mechanism
  • Unmanned aerial vehicle weak and small target detection method based on space-time attention mechanism
  • Unmanned aerial vehicle weak and small target detection method based on space-time attention mechanism

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Embodiment Construction

[0026] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0027] like Figure 1-Figure 3 As shown, the method for detecting weak and small targets of a UAV with a spatiotemporal attention mechanism of the present invention includes:

[0028] Step S1: Target feature extraction based on spatial attention: A typical deep learning target detection model consists of three parts: a backbone network, a feature pyramid and a decision-making mechanism. The shallow channels of the feature pyramid usually represent small-scale information, but their feature learning capabilities are limited. , the feature representation ability of the deep channel is stronger, but the small target features are easily lost as the convolution deepens, which will also increase the amount of computation. Based on this, the present invention makes the following improvements on the basis of selecting the same backbone network...

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Abstract

The invention discloses an unmanned aerial vehicle weak and small target detection method based on a space-time attention mechanism. The method comprises the following steps: S1, performing cascade transmission and multi-scale fusion target feature extraction based on space attention are carried out; s2, inputting an image feature sequence based on time attention: in combination with the time continuity of the spatial position and attitude features of the target in the continuous detection process, using a section of continuous feature sequence as input, and inputting the continuous feature sequence into a time attention structure built by a time sequence network to enhance the target information; s3, decision output based on space-time attention: outputting the target position and the category confidence according to the image features extracted by the space-time attention structure. The invention has the advantages of high efficiency, low cost, accurate data, flexible operation and the like.

Description

technical field [0001] The invention mainly relates to the technical field of unmanned aerial vehicles, in particular to a method for detecting weak and small targets of unmanned aerial vehicles with a spatiotemporal attention mechanism. Background technique [0002] Small UAVs carry visible light loads to implement intelligent image reconnaissance and scene perception in the area under their jurisdiction, and have the advantages of flexible use, high cost performance, and good environmental adaptation in agriculture, animal husbandry, forest fire prevention, and military applications. [0003] Subject to the terrain and climatic conditions of mountains / forests / plateaus, the image targets captured by drones often have only a dozen pixels, and their shape and texture are easily submerged in the background, which brings great challenges to target detection and tracking. At this stage, deep learning methods have achieved certain results in the field of target detection. Through...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46G06T7/00G06N3/04G06N3/08
CPCG06T7/0002G06N3/049G06N3/08G06T2207/10004G06T2207/20081G06T2207/20084G06V20/10G06V10/40G06N3/045
Inventor 孙备左震周靖苏绍璟郭晓俊魏俊宇蒋薇孙晓永谭晓朋
Owner NAT UNIV OF DEFENSE TECH
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