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Initial study on applicability of thermal imaging for microplastics identification in aquatic medium

E. MANIKANDAN1,* , SHEENA CHRISTABEL PRAVIN2, V. KIRUTHIKA2, S. GOPIKASHREE2, H. T. SAI GOKUL CHANDAN2, K. A. KARTHIGEYAN3, S. SHOBA4

Affiliation

  1. Centre for Advanced Materials and Innovative Technologies, Vellore Institute of Technology, Chennai Campus - Chennai, India
  2. School of Electronics Engineering, Vellore Institute of Technology, Chennai Campus – Chennai, India
  3. Department of ECE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology – Chennai, India
  4. Centre for Advanced Data Science, Vellore Institute of Technology, Chennai Campus – Chennai, India

Abstract

Owing to plastic's durability, plastic waste management is challenging. The current issue is that millions of tonnes of plastic end up in the ocean, possibly resulting in microplastic pollution and becoming the most prevalent underwater contaminant. Efficient identification of microplastic pollution is a scientific challenge because as the size of the particles decreases, it becomes more difficult to recognize and detect them. In this work, we demonstrate a deep learning-based approach for microplastics identification. The dataset is acquired using the thermal imaging technique. Data collection is followed by a data pre-processing step and finally, a classification step. The reported best accuracy, 97.42%, is achieved using the CNN model. The proposed approach allows for the deployment of increasingly diversified models as deep-learning models are still progressing..

Keywords

Microplastics, Pollution detection, Image classification, Deep learning, CNN.

Submitted at: Oct. 10, 2023
Accepted at: Feb. 3, 2025

Citation

E. MANIKANDAN, SHEENA CHRISTABEL PRAVIN, V. KIRUTHIKA, S. GOPIKASHREE, H. T. SAI GOKUL CHANDAN, K. A. KARTHIGEYAN, S. SHOBA, Initial study on applicability of thermal imaging for microplastics identification in aquatic medium, Journal of Optoelectronics and Advanced Materials Vol. 27, Iss. 1-2, pp. 77-82 (2025)