Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Target detection method based on information enhancement

A target detection and information enhancement technology, applied in the field of computer vision, can solve the problem of low accuracy

Active Publication Date: 2020-09-01
NAT UNIV OF DEFENSE TECH
View PDF7 Cites 12 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The technical problem to be solved by the present invention is to solve the shortcomings of the current single-stage detection method, although the detection speed is fast, but the accuracy is low

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Target detection method based on information enhancement
  • Target detection method based on information enhancement
  • Target detection method based on information enhancement

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0083] figure 1 Is the overall flow chart of the present invention. like figure 1 Shown, the present invention comprises the following steps:

[0084] Step 1: Build a target detection system. The system as figure 2 As shown, it consists of a feature extraction module, a semantic enhancement module, a feature selection module, a feature fusion module, and a detection module.

[0085] The feature extraction module is a convolutional neural network, which is connected with the semantic enhancement module. The feature extraction module includes a total of 23 convolutional layers and 5 pooling layers, with a total of 28 layers. The pooling layers are the 3rd, 6th, 10th, 14th, and 18th layers, and the other layers are convolutional layers. The feature extraction module receives the image I, performs feature extraction on the image I, obtains a multi-scale feature map set F(I), and sends F(I) to the semantic enhancement module. The multi-scale feature map set contains feature...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a target detection method based on information enhancement, and aims to overcome the defect of low precision of a single-stage detection method. According to the technical scheme, a target detection system composed of a feature extraction module, a semantic improvement module, a feature selection module, a feature fusion module and a detection module is constructed; a target detection network is trained by adopting the training data set; feature extraction, semantic improvement, feature selection and feature fusion are carried out on a single-frame image by adopting thetrained target detection system; and the position and category of the target are identified. The semantic enhancement module enriches semantic information of multi-scale features; the feature selection module enhances useful information in different scale feature maps by adopting an attention module, useless information is inhibited, and the purpose of information enhancement is achieved; the feature fusion module fuses the global semantic feature map subjected to feature selection to the multi-scale feature map, so that each feature map has more accurate position and semantic information, and the detection precision is improved.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to an information enhancement-based target detection method. Background technique [0002] Target detection is one of the important research directions in the field of computer vision. The traditional target detection method is to extract features by constructing feature descriptors (such as histograms of orientation gradients, etc.) for images in a certain area, and then use classifiers to classify the features. Target detection, such as support vector machine SVM (Support Vector Machine), etc. Recently, with the development of convolutional neural networks, engineering features have mostly been replaced by convolutional neural network features, and object detection systems have made great progress in both accuracy and speed. [0003] Currently, object detection methods based on deep learning are divided into two-stage detection methods and single-stage detection methods. ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06V10/40G06V2201/07G06N3/048G06N3/045G06F18/25G06F18/214
Inventor 史殿习崔玉宁刘哲杨思宁李林
Owner NAT UNIV OF DEFENSE TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products