Plastic classification base on correlation of RGB color

Srigul, Witaya; Inrawong, Prajuab; Kupimai, Mongkol

doi:10.1109/ecticon.2016.7561304

Cited by 14 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some improvements were made to the method. The size of the elements of the diagonal matrix added to the Gauss-Newton Hessian was adjusted according to the size of the ratio between actual decrease and predicted decrease (15) and (16).…”

Section: Adaptive Methods For Correcting Measured Color Intensity Vmentioning

confidence: 99%

“…Automated inspection based on machine vision has been successfully applied to detect surface defects in electronic device packages [13] and plastic products [14], problematic printing processes [10], color defects in fruit skins [15], and to classify plastic in recycling processes [16], [17], etc. Most of these systems inspect objects that are stage positioned and require mechatronic equipment to capture image orientation.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An Adaptive Method for Inspecting Illumination of Color Intensity in Transparent Polyethylene Terephthalate Preforms

et al. 2020

View full text Add to dashboard Cite

Machine vision systems are applied in industry to control the quality of production while optimizing efficiency. A machine vision and AI-based inspection of color intensity in transparent Polyethylene Terephthalate (PET) preforms is especially sensitive to backgrounds and lighting, therefore, much attention is given to its illumination conditions. The paper examines the adverse factors affecting the quality of image recognition and presents an adaptive method for reducing the influence of changing illumination conditions in the color inspection process of transparent PET preforms. The method is based on predicting measured color intensity correction parameters according to illumination conditions. To test this adaptive method, a hardware and software system for image capture and processing was developed. This system is capable of inspecting large quantities of preforms in real time using a neural network with a modified gradient descent and momentum algorithm. The experiment showed that correction of the measured color intensity value reduced the standard deviation caused by variable and uneven illumination by 61.51%, demonstrating that machine vision color intensity evaluation is a robust and adaptive solution under illuminated conditions for detecting abnormalities in machine-based PET inspection procedures. INDEX TERMS Image processing, machine vision, neural nets, data mining.

show abstract

Section: Adaptive Methods For Correcting Measured Color Intensity Vmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Adaptive Method for Inspecting Illumination of Color Intensity in Transparent Polyethylene Terephthalate Preforms

et al. 2020

View full text Add to dashboard Cite

show abstract

“…From this research, it is found that using RGB color has good accuracy. Researcher [11], used RGB color to classify the types of plastic bottles with testing data of 200 bottles. The results of the classification of the plastic bottles, RGB color, have an accuracy of 93.33%.…”

Section: Introductionmentioning

confidence: 99%

Image Processing System for pH Classification Using Biosensors

Aziz¹,

Sujito²,

Wibawa³

et al. 2021

Atlantis Highlights in Engineering

View full text Add to dashboard Cite

The biosensor in this study used a butterfly pea flower. Butterfly pea flowers require a classification system because the colors produced in detecting pH are similar. The method used in this research is using image processing to get the RGB value to be analyzed using the Raspberry Pi 4. The operation and results of the pH classification will be displayed on the TFT LCD. The hardware design is designed in a closed manner with LED lighting. The results of this research on the hardware function well because it can take the pH color of the biosensor with the lighting obtained from the LED. While the results of the classification system with 10 trial samples of mineral water, soft drinks, water, detergent, clothes perfume, and chemical solutions obtained an accuracy of 88.55%. and can display on the TFT LCD.

show abstract

“…Although plastic is the most widely used inorganic material globally, particularly in countries experiencing rapid economic growth (Srigul et al, 2016) , plastic can be harmful to the environment due to its hundreds-year decomposition time (Shuai et al, 2020) . Recycling is a viable option for managing and reducing plastic waste instead of land lls and incineration (Chow et al, 2016) .…”

Section: Introductionmentioning

confidence: 99%

Prediction of Plastic-Type for Sorting System using Fisher Discriminant Analysis

Yani

Resti

Burlian

et al. 2021

Sci. Technol. Indones

View full text Add to dashboard Cite

Recycling is a more environmentally friendly method of managing and reducing plastic waste that can significantly reduce land degradation, pollution, and greenhouse gas emissions. According to its composition, an essential first step in the recycling process is sorting out plastic waste. However, inadequate sorting of plastic types can result in cross-contamination and increasing industrial operating costs. A low-cost automated plastic sorting system can be developed by using digital image data in the red, green, and blue (RGB) color space as the dataset and predicting the type using learning datasets. The purpose of this paper is to demonstrate how to use Fisher Discriminant Analysis (FDA) to predict the plastic type from a digital image of the RGB model and then evaluate the performance using cross-validation. This work has four main steps: collecting plastic digital image data, forming statistical tests, predicting plastic types, and evaluating prediction performance. FDA is quite effective for predicting the type of plastic. Performance measures the accuracy of 87.11 %, the recall-micro of 91.67 %, the recall-micro of 80.97 %, the specificity-micro of 90.33 %, and the specificity-macro of 90.38 %, respectively. The micro is determined by the number of decisions made for each object. In comparison, the macro is calculated based on the average decision made by each class.

show abstract

Plastic classification base on correlation of RGB color

Cited by 14 publications

References 7 publications

An Adaptive Method for Inspecting Illumination of Color Intensity in Transparent Polyethylene Terephthalate Preforms

An Adaptive Method for Inspecting Illumination of Color Intensity in Transparent Polyethylene Terephthalate Preforms

Image Processing System for pH Classification Using Biosensors

Prediction of Plastic-Type for Sorting System using Fisher Discriminant Analysis

Contact Info

Product

Resources

About