Conveyor belt carrier roller detection method based on YOLOv3

A detection method and conveyor belt technology, applied in the field of computer vision and deep learning, can solve the problems of low detection accuracy of small targets, large training parameters, and large amount of calculation, so as to meet the real-time detection requirements, meet the accuracy requirements, The effect of improving detection accuracy

Pending Publication Date: 2020-10-09
HENAN UNIV OF SCI & TECH
View PDF0 Cites 10 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The methods based on candidate frames mainly include R-CNN, Fast-RCNN, Faster-RCNN and other methods. Although these methods have high detection accuracy, they have a large amount of calculation and slow detection speed, so they cannot detect targets in real time; regression-based target detection methods , there are mainly two series of methods, SSD and YOLO. Although the SSD algorithm

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
  • Conveyor belt carrier roller detection method based on YOLOv3
  • Conveyor belt carrier roller detection method based on YOLOv3
  • Conveyor belt carrier roller detection method based on YOLOv3

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0052] Example 1

[0053] Such as figure 1 Shown is the overall flow chart of the training and testing of the present invention, which specifically includes the following steps:

[0054] S1. Before training, first prepare the data set required for training, collect belt idler pictures under different lighting, temperature and other environments to make an initial sample data set; specifically, step S1 includes:

[0055] S11. By installing cameras at different positions on both sides of the belt, real-time shooting of the picture information of the idler when the belt is rotating;

[0056] S12. Perform frame division processing on the acquired idler video, extract images of each frame, and obtain idler image sequence sets in different time periods;

[0057] S13. Filter the sequence set obtained in step S12, and select the roller image sequence set under different illumination, different time period, different weather, etc.; to obtain an initial sample data set.

[0058] S2. Perform prepro...

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 conveyor belt carrier roller detection method based on YOLOv3. The method belongs to the field of computer vision and deep learning. The method comprises the following steps: replacing a feature extraction network darknet53 of YOLOv3 with a lightweight feature extraction network Mobilenet; and frame loss and center loss in the YOLOv3 loss function are replaced with GIOUloss, and an improved YOLOv3-Mobilenet belt carrier roller detection model is constructed. And training the model on a training set, testing the performance of the model on a test set, and comparing the performance test result with the performance of other models. The target recognition method provided by the invention is strong in generalization ability, realizes effective detection of the carrier roller, provides effective guarantee for subsequent judgment of whether the belt is separated from the track and monitoring of the running state, reduces the parameter calculation amount, and improves the speed and accuracy of an original YOLOv3 target detection model.

Description

Technical field [0001] The invention belongs to the field of computer vision and deep learning, and particularly relates to a method for detecting the running state of a conveyor belt idler roller. Background technique [0002] The roller is an important part of the conveyor belt. The main function is to support the weight of the conveyor belt and the material. Due to the high loss and failure rate of the conveyor belt roller, the roller is prone to deformation and causes greater safety hazards; for a long time, the failure of the roller depends on manual labor. The patrol inspection has high work intensity and serious missed inspections, and it is impossible to effectively monitor its operating status; therefore, the realization of the intelligent detection of the conveyor belt rollers is of great significance for the subsequent fault diagnosis of the rollers and the intelligent transformation of the production line. [0003] At present, the mainstream target detection methods bas...

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): G06T7/00G06K9/62G06N3/04
CPCG06T7/0004G06T2207/10016G06N3/045G06F18/23213G06F18/214
Inventor 马建伟候向关臧绍飞叶永斌牛怡雯
Owner HENAN UNIV OF SCI & TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap