Method for rapidly detecting small target under view angle of unmanned aerial vehicle based on yolov3

Abstract

The invention discloses a method for rapidly detecting a small target under a view angle of an unmanned aerial vehicle based on yolov3, and the method comprises the steps: obtaining an image of a to-be-detected scene, inputting the image into a trained small target detection model, and outputting a detection result of a small target, wherein the model building process comprises the following steps of: adding a prediction unit on a quadruple down-sampled feature map output by a second group of residual blocks in the Darknet53, wherein the prediction unit comprises two residual units which are connected in sequence; carrying out 2-time up-sampling operation on the feature map output by the 8-time down-sampling prediction branch output by the third group of residual blocks, and then carrying out feature fusion operation on the feature map and the feature map output by the prediction unit; adding two sequentially connected residual units behind the feature map output by the second group of residual blocks of the YOLOv3 network so as to establish a small target detection network; and training the small target detection network by using the preprocessed data set, and then carrying out network pruning on the trained small target detection network to obtain the small target detection model.

Description

technical field

[0001] The invention relates to the technical field of target detection, in particular to a yolov3-based rapid detection method for small targets under the perspective of an unmanned aerial vehicle, combined with model compression technology, to realize rapid detection of small-scale targets on mobile embedded devices. Background technique

[0002] As an emerging technology, UAV target detection has a wide range of applications in aerial image analysis, intelligent monitoring, and route detection. Object detection has made great progress in recent years, especially with the development of large-scale visual datasets and the improvement of computing power, deep neural networks (DNNs), especially convolutional neural networks (CNNs), demonstrated record-breaking performance in computer vision tasks. However, it is still a challenging work due to the view-specific target scale issue.

[0003] At present, some excellent target detection algorithms have achieved...

Examples

Embodiment Construction

[0042] see figure 1 , the present invention discloses a method for fast detection of small targets based on yolov3 from the perspective of unmanned aerial vehicle, comprising the following steps:

[0043] Obtain the image of the scene to be detected, input it into the trained small target detection model, and output the detection result of the small target;

[0044] The establishment process of the small target detection model includes:

[0045]Add a prediction unit to the 4 times downsampled feature map output by the second group of residual blocks in the backbone network Darknet53 of the YOLOv3 network, which includes two sequentially connected residual units; the third group of residual blocks in the backbone network Darknet53 The group residual block outputs the feature map output by the 8 times downsampling prediction branch, first performs the 2 times upsampling operation, and then performs the 4 times downsampling feature map output by the prediction unit after the sec...

technical field

[0001] The invention relates to the technical field of target detection, in particular to a yolov3-based rapid detection method for small targets under the perspective of an unmanned aerial vehicle, combined with model compression technology, to realize rapid detection of small-scale targets on mobile embedded devices. Background technique

[0002] As an emerging technology, UAV target detection has a wide range of applications in aerial image analysis, intelligent monitoring, and route detection. Object detection has made great progress in recent years, especially with the development of large-scale visual datasets and the improvement of computing power, deep neural networks (DNNs), especially convolutional neural networks (CNNs), demonstrated record-breaking performance in computer vision tasks. However, it is still a challenging work due to the view-specific target scale issue.

[0003] At present, some excellent target detection algorithms have achieved...

Examples

Embodiment Construction

[0042] see figure 1 , the present invention discloses a method for fast detection of small targets based on yolov3 from the perspective of unmanned aerial vehicle, comprising the following steps:

[0043] Obtain the image of the scene to be detected, input it into the trained small target detection model, and output the detection result of the small target;

[0044] The establishment process of the small target detection model includes:

[0045]Add a prediction unit to the 4 times downsampled feature map output by the second group of residual blocks in the backbone network Darknet53 of the YOLOv3 network, which includes two sequentially connected residual units; the third group of residual blocks in the backbone network Darknet53 The group residual block outputs the feature map output by the 8 times downsampling prediction branch, first performs the 2 times upsampling operation, and then performs the 4 times downsampling feature map output by the prediction unit after the sec...

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