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Target area detection method based on deep learning

A technology of target area and deep learning, which is applied in the improvement of deep learning image detection methods, and in the field of traditional image processing, can solve problems such as confusion and difficult target detection

Active Publication Date: 2019-06-07
BEIJING UNIV OF TECH
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  • Abstract
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AI Technical Summary

Problems solved by technology

Target detection is not difficult for humans. It is easy to locate and classify the target objects through the perception of different color modules in the picture, but for computers, it is difficult to get directly from the image because of the RGB pixel matrix Abstract concepts such as dogs and cats and their positions are located, and sometimes multiple objects and cluttered backgrounds are mixed together, making target detection more difficult
The core problems to be solved by target detection are: 1. The target may appear anywhere in the image
3. Targets may come in various shapes

Method used

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  • Target area detection method based on deep learning

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Embodiment Construction

[0041] The present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0042] The used hardware equipment of the present invention has 1 PC machine, 1 nvidia1080 graphics card;

[0043] Such as figure 1 As shown, the present invention provides a target area detection method based on deep learning, and the following is the specific content of the experiment under coco2017. Specifically include the following steps:

[0044] Step 1, get the coco2017 image dataset. and clean the data. Since the 2017coco dataset is a relatively clean public dataset, the pictures were not deleted.

[0045] Step 2, image preprocessing, because each image in the coco dataset is labeled, so all the data are image enhanced. Images undergo data augmentation with 50% probability. The data enhancement used mainly includes rotation, flip, contrast enhancement, cropping, brightness, and affine transformation. ...

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Abstract

The invention discloses a target area detection method based on deep learning, and belongs to the technical field of computer vision. The method mainly adopts a retinanet detection network. The RetinaNet is essentially a network structure composed of two FCN sub-networks of resnet + FPN +. ResNeXt50 and densenet 169 are respectively adopted by the backbone to replace the previous resnet. An FPN layer and a loss function of the retnanet network are modified, and finally model fusion is carried out. The target detection method combines the advantages of a current mainstream target detection method and already solves a series of practical problems. According to the algorithm, an experiment is carried out under the cococo2017, and the performance is very good. And the method is better than a single model under retinanet and a result obtained when the model is not improved. In addition, the method has good performance on other data sets.

Description

technical field [0001] The invention belongs to the technical field of computer vision, mainly relates to the improvement of deep learning image detection methods, and involves some traditional image processing. Background technique [0002] With the development of artificial intelligence, the application of computer vision has also been vigorously developed. In computer vision applications, image detection is an important branch, and image target detection is of great significance in fields such as face recognition, unmanned driving, unmanned retail, and intelligent medical care. [0003] Image target detection is an important research direction in computer vision. With the development of deep learning, target detection technology has made great progress. Target detection is not difficult for humans. It is easy to locate and classify the target objects through the perception of different color modules in the picture, but for computers, it is difficult to get directly from ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00
Inventor 张涛郝兵冯宇婷
Owner BEIJING UNIV OF TECH
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