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

Real-time medicine box detection method based on YOLOv3 pruning network and embedded development board

A detection method and development board technology, which is applied in the field of image processing, can solve the problems of large convolutional neural network parameters and the inability to smoothly deploy embedded devices and mobile devices, so as to reduce the amount of calculation and storage, and maintain high performance , the effect of reducing the size

Pending Publication Date: 2021-04-02
ZHEJIANG UNIV OF TECH
View PDF0 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Although the YOLO series models have greatly improved the detection speed and reduced the model size, due to the huge amount of parameters of the convolutional neural network itself, it still cannot be successfully deployed on low-configuration embedded devices and mobile devices.

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
  • Real-time medicine box detection method based on YOLOv3 pruning network and embedded development board
  • Real-time medicine box detection method based on YOLOv3 pruning network and embedded development board
  • Real-time medicine box detection method based on YOLOv3 pruning network and embedded development board

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0047] Further illustrate the present invention below in conjunction with accompanying drawing.

[0048] The real-time medicine box detection method based on YOLOv3 pruning network and embedded development board of the present invention, specific process is as follows:

[0049] Step 1: YOLOv3 backbone network design, such as Figure 1 shown;

[0050] Step 1-1: In theory, the deeper the network, the better the detection effect and the higher the accuracy rate, but the experimental results show that excessive increase in the number of network layers will cause the network to fall into overfitting and make the network converge Slower, lower detection accuracy, and more difficult to deploy on embedded devices because of the increased computational cost of the model. To solve this problem, the YOLOv3 backbone network draws on the layer-hopping connection structure of the deep residual network. In order to reduce the influence of the pooling layer on the gradient calculation, the...

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 relates to a real-time medicine box detection method based on a YOLOv3 pruning network and an embedded development board. The method comprises the following steps: step 1, designing a YOLOv3 backbone network and a loss function; 2, collecting image data of the medicine boxes of all brands in a manual shooting mode; 3, making a medicine box data set and performing training; 4, performing model compression and acceleration calculation on the YOLOv3 through a pruning method based on a BN layer scaling factor gamma; 5, deploying the YOLOv3 compression model to a Nano embedded system,and carrying out model reasoning acceleration by using TensorRT; and step 6, carrying out real-time medicine box detection on the Nano by using a CSI camera. The invention is used for being deployedon an NVIDIA Jetson Nano embedded development board to carry out real-time medicine box detection, and the real-time performance of detection and the high efficiency of model operation are ensured while the detection precision is ensured.

Description

technical field [0001] The invention belongs to an image processing technology based on deep learning, in particular to a real-time medicine box detection method based on a YOLOv3 pruning network and an embedded development board. Background technique [0002] Real-time object detection technology is a research hotspot in the field of computer vision in recent years. This technology includes the design of lightweight object detection networks, the production of target data sets, and the research on model deployment carriers. At present, real-time target detection technology based on image sequences can realize computer observation and detection of targets in image sequences. This technology is representative in future intelligent driving and computer intelligent sorting. Among them, one of the most potential applications lies in the field of real-time and fast intelligent sorting, such as the robot intelligent sorting of medicine boxes on an unmanned assembly line. [0003]...

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): G06K9/00G06K9/32G06K9/62G06N3/04G06N3/08
CPCG06N3/082G06V20/00G06V10/25G06N3/045G06F18/23213G06F18/241
Inventor 禹鑫燚曹铭洲张铭扬欧林林戎锦涛
Owner ZHEJIANG UNIV OF 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

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