RGB and RGNIR image dataset for machine learning in plastic waste detection

被引：0

作者：

Tamin, Owen ^{[1
]}

Moung, Ervin Gubin ^{[2
,3
]}

Dargham, Jamal Ahmad ^{[4
]}

Karim, Samsul Ariffin Abdul ^{[5
]}

Ibrahim, Ashraf Osman ^{[6
,7
]}

Adam, Nada ^{[8
]}

Osman, Hadia Abdelgader ^{[8
]}

机构：

[1] Univ Malaysia Sabah, Fac Sci & Nat Resources, Jalan UMS, Kota Kinabalu 88400, Sabah, Malaysia

[2] Univ Malaysia Sabah, Fac Comp & Informat, Data Technol & Applicat DaTA Res Grp, Jalan UMS, Kota Kinabalu 88400, Sabah, Malaysia

[3] Univ Malaysia Sabah, Fac Comp & Informat, Jalan UMS, Kota Kinabalu 88400, Sabah, Malaysia

[4] Univ Malaysia Sabah, Fac Engn, Kota Kinabalu 88400, Sabah, Malaysia

[5] Univ Utara Malaysia, Inst Strateg Ind Decis Modelling ISIDM, UUM Coll Arts & Sci, Sch Quantitat Sci, Sintok 06010, Kedah Darul Ama, Malaysia

[6] Univ Teknol PETRONAS, Dept Comp & Informat Sci, Seri Iskandar 32610, Malaysia

[7] Univ Teknol PETRONAS, Emerging & Digital Technol Inst, Posit Comp Res Ctr, Seri Iskandar 32610, Malaysia

[8] Northern Border Univ, Appl Coll, Dept Comp Sci, Ar Ar 73213, Saudi Arabia

来源：

DATA IN BRIEF | 2025年 / 60卷

关键词：

Plastic waste detection; Machine learning; Spectral imaging; Colour spaces; RGB; RGNIR; Plastic waste dataset; Automatic sorting methods; Deep learning; Environmental issues;

D O I：

10.1016/j.dib.2025.111524

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

The increasing volume of plastic waste is an environmental issue that demands effective sorting methods for different types of plastic. While spectral imaging offers a promising solution, it has several drawbacks, such as complexity, high cost, and limited spatial resolution. Machine learning has emerged as a potential solution for plastic waste due to its ability to analyse and interpret large volumes of data using algorithms. However, developing an efficient machine learning model requires a comprehensive dataset with information on the size, shape, colour, texture, and other features of plastic waste. Moreover, incorporating near-infrared (NIR) spectral data into machine learning models can reveal crucial information about plastic waste composition and structure that remains invisible in standard RGB images. Despite ( http://creativecommons.org/licenses/by-nc/4.0/ )

引用

页数：10

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