Affiliation:
1. Department of Sustainable Engineering Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences Chennai India
2. Department of Information Technology Rajagiri School of Engineering and Technology Kochi India
3. Department of Research Rajalakshmi Institute of Technology Chennai India
4. Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India
5. Department of Information Technology Karpagam College of Engineering Coimbatore India
Abstract
AbstractThe contamination of microplastics (MPs) creates a substantial risk to both the environment and human health, necessitating the development of efficient methods for detecting and categorizing these micro pollutant particles. As a solution, Dense‐UNet with Convolutional Vision Transformer (Dense‐UNet‐CvT), a novel deep learning (DL)‐based model is proposed to detect and classify the MPs by performing the computer vision tasks. The main objective of this work is to enhance the detection accuracy in detecting the MPs classified from the input images. Initially, a holographic MPs image dataset comprising primary classes such as polyethylene (PE), polystyrene (PS), low‐density polyethylene (LDPE), polyhydroxyalkanoate (PHA) is collected for training and evaluating the research model. The images from the dataset are preprocessed by performing image resizing, Recursive Exposure based Sub‐Image Histogram Equalization (RESIHE)‐based image enhancement, Gaussian Adaptive Bilateral Filtering (GABF)‐based denoising to improve the visual quality of the images. The preprocessed images are applied for segmentation using the Dense‐UNet model for performing semantic segmentation. The CvT model is implemented to extract useful features and to perform classification on detecting the known and unknown classes of MPs labeled in the collected dataset. The MPs detection and classification performances are computed in terms of detection rate, accuracy, f1‐score, and precision. The Dense‐UNet‐CvT model achieved 98.22% detection rate, 98.59% accuracy, 98.35% f1‐score, and 98.76% precision. These performances are compared with the current models for proper validation, in which the research model outperformed all the compared models in terms of performance. Overall, the proposed Dense‐UNet‐CvT model demonstrates superior performance across multiple evaluation metrics, suggesting its effectiveness in detecting and classifying MPs contamination in holographic images.