Design of servo drive system for machine vision inspection platform

With the more application of machine vision technology in production practice, most machine vision systems are based on passive vision to measure the target, which has some limitations. Based on the requirements of machine vision platform, a three-dimensional servo movement scheme based on active positioning vision is proposed in this paper. In this paper, the parts of the servo drive system of the platform are selected, calculated and checked, the three-dimensional modeling of the machine vision platform is completed in SolidWorks, and the motion simulation of the servo control system is carried out.

Design of On-line Detecting Device for Groove Wear of Elevator Traction Wheel

After the wear of elevator traction wheel, there is a traditional detection method which can measure the geometric dimensions of wheel groove. But this method has low measurement accuracy and cannot accurately reflect the actual profile of wheel groove. By means of the machine vision inspection technology, the profile of the traction wheel groove can be measured, a new detection method is proposed in this paper, and the corresponding measuring device is designed in detail. At the same time, with help of the computer simulation, the processing of the work piece image is used. In this method, the size and profile of the wheel groove of the traction wheel can be measured more accurately and quickly to reflect the actual wear value of the wheel groove.

Cotton Gin Stand Machine-Vision Inspection and Removal System for Plastic Contamination: Software Design

The removal of plastic contamination from cotton lint is an issue of top priority to the U.S. cotton industry. One of the main sources of plastic contamination showing up in marketable cotton bales is plastic used to wrap cotton modules produced by John Deere round module harvesters. Despite diligent efforts by cotton ginning personnel to remove all plastic encountered during module unwrapping, plastic still finds a way into the cotton gin’s processing system. To help mitigate plastic contamination at the gin, a machine-vision detection and removal system was developed that utilizes low-cost color cameras to see plastic coming down the gin-stand feeder apron, which upon detection, blows plastic out of the cotton stream to prevent contamination. This paper presents the software design of this inspection and removal system. The system was tested throughout the entire 2019 cotton ginning season at two commercial cotton gins and at one gin in the 2018 ginning season. The focus of this report is to describe the software design and discuss relevant issues that influenced the design of the software.

Fast semantic segmentation method for machine vision inspection based on a fewer-parameters atrous convolution neural network

Owing to the recent development in deep learning, machine vision has been widely used in intelligent manufacturing equipment in multiple fields, including precision-manufacturing production lines and online product-quality inspection. This study aims at online Machine Vision Inspection, focusing on the method of online semantic segmentation under complex backgrounds. First, the fewer-parameters optimization of the atrous convolution architecture is studied. Atrous spatial pyramid pooling (ASPP) and residual network (ResNet) are selected as the basic architectures of ηseg and ηmain, respectively, which indicate that the improved proportion of the participating input image feature is beneficial for improving the accuracy of feature extraction during the change of the number and dimension of feature maps. Second, this study proposes five modified ResNet residual building blocks, with the main path having a 3 × 3 convolution layer, 2 × 2 skip path, and pooling layer with ls = 2, which can improve the use of image features. Finally, the simulation experiments show that our modified structure can significantly decrease segmentation time Tseg from 719 to 296 ms (decreased by 58.8%), with only a slight decrease in the intersection-over-union from 86.7% to 86.6%. The applicability of the proposed machine vision method was verified through the segmentation recognition of the China Yuan (CNY) for the 2019 version. Compared with the conventional method, the proposed model of semantic segmentation visual detection effectively reduces the detection time while ensuring the detection accuracy and has a significant effect of fewer-parameters optimization. This slows for the possibility of neural network detection on mobile terminals.