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Improved YOLOv3 minimum remote sensing image target detection method and device and storage medium

A remote sensing image and target detection technology, applied in the field of target detection, can solve the problems of high false alarm rate, slow detection speed, and low detection rate of extremely small remote sensing image targets, and achieve the effect of improving detection speed and detection performance

Active Publication Date: 2020-07-28
UNIV OF SHANGHAI FOR SCI & TECH
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  • Application Information

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Problems solved by technology

[0004] Aiming at the deficiencies of the prior art, the present invention discloses an improved YOLOv3 minimal remote sensing image target detection method, device and storage medium, which are used to solve the problem of low target detection rate, high false alarm rate, and detection speed of minimal remote sensing image targets at the current stage. slow problem

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  • Improved YOLOv3 minimum remote sensing image target detection method and device and storage medium
  • Improved YOLOv3 minimum remote sensing image target detection method and device and storage medium
  • Improved YOLOv3 minimum remote sensing image target detection method and device and storage medium

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Embodiment 1

[0028] The present embodiment discloses a method for improving YOLOv3's target detection in extremely small remote sensing images, the method comprising the following steps:

[0029] S1 obtains the target data and fuses the output of the convolutional layer features of the YOLOv3 network to form a pyramid feature layer;

[0030] S2 combines the feature output of the shallow convolutional layer of YOLOv3 to form a pyramid feature layer;

[0031] S3 fuses the pyramid feature layer with two-way combination;

[0032] S4 changed the downsampling layer of the YOLOv3 network to a 3×3 convolutional layer;

[0033] S5 uses 1×1 convolution to reduce the dimension of the network model and output data.

[0034] In the above S1, the feature output of the convolution layer of the last layer of the YOLOv3 network is fused with the feature output of the convolution layer of the adjacent upper layer to form a top-down pyramid feature layer.

[0035] In the above S2, the feature output of th...

Embodiment 2

[0039] This embodiment discloses a minimal remote sensing image target detection device that improves YOLOv3, including an FPN module, a memory, a processor, and a computer program stored on the memory and operable on the processor, the computer program When executed by the processor, the improved YOLOv3 minimal remote sensing image target detection method described in the above-mentioned embodiments is realized.

[0040] Additional bottom-up, laterally connected paths are added to the FPN module.

[0041] This embodiment also discloses a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the improved YOLOv3 minimal remote sensing image target detection method described in the above embodiment is implemented.

Embodiment 3

[0043]In this embodiment, image classification can predict the category of a single object in the image, and object detection has its own challenges, and it is necessary to predict multiple object categories and corresponding positions in a single image. In order to solve this problem, a pyramid feature method that represents the target feature layer at multiple scales is proposed, and the feature pyramid network is one of the representative model architectures for generating pyramid feature representations for target detection. It uses top-down and horizontal connections to combine two adjacent feature layers in the backbone network model to construct a feature pyramid to generate high-resolution, strong semantic features. YOLOv3 draws on the idea of ​​the above feature pyramid model, such as Figure 4 shown. It is a backbone network model based on Darknet53, with a total of 75 convolutional layers, 5 residual blocks, and 5 times of downsampling, which use the 8 times, 16 ti...

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Abstract

The invention relates to the technical field of target detection, in particular to an improved YOLOv3 minimum remote sensing image target detection method and device and a storage medium. According tothe invention, an additional bottom-up and transverse connection path is added to the FPN module to improve the performance of the low-resolution feature; a top-down and bottom-up feature pyramid network is constructed, a bidirectionally combined pyramid feature layer is fused and applied to target detection of a remote sensing image, the dimension of a network model is reduced by adopting 1 * 1convolution, and the detection speed of the network is improved. And finally, quantitative and qualitative comparative analysis is carried out on VEDAI and NWPU VHR remote sensing vehicle data sets and the most advanced YOLOv3 network. The result shows that the detection performance of the improved network is obviously improved compared with the original network, the detection speed of the networkis hardly changed, and the problems of low target detection rate, high false alarm rate and low detection speed of the minimum remote sensing image at the present stage are solved.

Description

technical field [0001] The present invention relates to the technical field of target detection, in particular to an improved YOLOv3 method, device and storage medium for detecting targets in extremely small remote sensing images. Background technique [0002] In recent years, target detection has become a research hotspot in computer vision. It is widely used in many fields such as robot navigation, intelligent video surveillance, industrial inspection, aerospace, etc. It not only needs to determine the category of the target in the image, but also needs to give its precise Location. [0003] In the past few years, researchers have found that designing a deep convolutional neural network can greatly improve the performance of image classification and object detection, because it can not only extract strong semantic high-level features of objects from images, but also combine feature extraction, feature Selection and feature classification are fused into one model, and thro...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06N3/04G06N3/08
CPCG06T7/0002G06N3/08G06T2207/10032G06T2207/20016G06T2207/20081G06N3/045
Inventor 张孙杰陈磊肖寒臣
Owner UNIV OF SHANGHAI FOR SCI & TECH
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