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 Elsayed; Nada Adam; Hadia Abdelgader Osman (Elsevier Inc., 2025)
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.