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

Target detection method based on lightweight convolutional neural network

A technology of convolutional neural network and target detection, which is applied in the field of target detection based on lightweight convolutional neural network, can solve problems such as slow speed, poor detection effect of small targets, complex network, etc., to overcome slow detection speed and realize The effect of real-time object detection

Active Publication Date: 2020-02-11
XIDIAN UNIV
View PDF5 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to address the above-mentioned deficiencies in the prior art, and propose a target detection method based on a lightweight convolutional neural network to solve the problem of existing target detection The method network is complex, the speed is not fast, and the detection effect on small targets is poor

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 lightweight convolutional neural network
  • Target detection method based on lightweight convolutional neural network
  • Target detection method based on lightweight convolutional neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0028] The present invention will be further described below with reference to the accompanying drawings.

[0029] combined with figure 1 The implementation steps of the present invention are further described.

[0030] Step 1, build a lightweight convolutional neural network.

[0031] The first step is to build a 9-layer feature extraction module, the structure of which is: the first convolutional layer → the second convolutional layer → the first pooling layer → the third convolutional layer → the fourth convolutional layer → the second Pooling layer → fifth convolutional layer → sixth convolutional layer → seventh convolutional layer; and set the parameters of each layer as: set the number of convolution kernels in the first to seventh convolutional layers to 64 respectively, 64, 128, 128, 256, 256, 256, the size of the convolution kernel is set to 3 × 3, the stride is set to 1, the first and second pooling layers use the maximum pooling method, and the pooling area is T...

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 a lightweight convolutional neural network, and mainly solves the problems that an existing target detection method is complex in network, low in speed and poor in small target detection effect. The method comprises the following specific steps: (1) constructing a lightweight convolutional neural network; (2) generating a target training set; (3) training a lightweight convolutional neural network; and (4) detecting the target to be detected. According to the invention, a lightweight convolutional neural network composed of a feature extraction module, a feature enhancement module and an identification and positioning module is constructed; the problems of good large target detection effect, poor small target detection effect and low speed in the existing target detection method are solved, so that the large target and the small target can be identified in real time.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a target detection method based on a lightweight convolutional neural network in the technical field of image recognition. The present invention can be used to detect stationary objects in natural images. Background technique [0002] A large number of existing object detection methods have achieved very high scores in public datasets, but there are still many challenges in object detection in real-world tasks, such as poor detection of small objects. For example, in intelligent physical education, detecting the ball will help the coach to grasp the accuracy of the students playing the ball. However, when the camera is placed in the sports room, the ball occupies only a few pixels, and the small ball in motion will be deformed or occluded by some specific movements of the players, and there are also different lighting, motion blur and other influencing factors , ...

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): G06K9/62
CPCG06V2201/07G06F18/214Y02D10/00
Inventor 谢雪梅金星石光明
Owner XIDIAN UNIV
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

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com