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

Owen Tamin; Ervin Gubin Moung; Jamal Ahmad Dargham; Samsul Ariffin Abdul Karim; Ashraf Osman Ibrahim; Nada Adam; Hadia Abdelgader Osman

doi:10.1016/j.dib.2025.111524

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

Data Brief. 2025 Mar 27:60:111524. doi: 10.1016/j.dib.2025.111524. eCollection 2025 Jun.

Authors

Owen Tamin¹, Ervin Gubin Moung^{2

3}, Jamal Ahmad Dargham⁴, Samsul Ariffin Abdul Karim⁵, Ashraf Osman Ibrahim^{6

7}, Nada Adam⁸, Hadia Abdelgader Osman⁸

Affiliations

¹ Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu, 88400, Sabah, Malaysia.
² Data Technologies and Applications (DaTA) Research Group, Faculty of Computing and Informatics, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu, 88400, Sabah, Malaysia.
³ Faculty of Computing and Informatics, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu, 88400, Sabah, Malaysia.
⁴ Faculty of Engineering, Universiti Malaysia Sabah, Kota Kinabalu, 88400, Sabah, Malaysia.
⁵ Institute of Strategic Industrial Decision Modelling (ISIDM), School of Quantitative Sciences, UUM College of Arts and Sciences, Universiti Utara Malaysia, 06010 Sintok, Kedah Darul Aman, Malaysia.
⁶ Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Malaysia.
⁷ Positive Computing Research Center, Emerging & Digital Technologies Institute, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Malaysia.
⁸ Department of Computer Science, The applied College, Northern Border University, Arar 73213, Saudi Arabia.

Abstract

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 this potential, no publicly available dataset currently combines RGB with NIR spectral information for plastic waste detection. To address this research gap, we introduce a comprehensive dataset of plastic waste images captured onshore using both standard RGB and RGNIR (red, green, near-infrared) channels. Each of the two-colour space datasets include 405 images that were taken along riverbanks and beaches. Both datasets underwent further pre-processing to ensure proper labelling and annotations to prepare them for training machine learning models. In total, there are 1,344 plastic waste objects that have been annotated. The proposed dataset offers a unique resource for researchers to train machine learning models for plastic waste detection. While there are existing datasets on plastic waste, the proposed dataset aims to set itself apart by offering a more comprehensive dataset with unique spectral information in the near-infrared region. It is hopeful that these datasets will contribute to the advancement of the field of plastic waste detection and encourage further research in this area.

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