Deep Classification of Microplastics Through Image Fusion Techniques
Paolo Russo, Fabiana Di Ciaccio · IEEE Access · 2024
Microplastics from fiber shredding are recognized by the scientific community as one of the main sources of microplastic water pollution. Therefore, there is a compelling need for techniques capable of accurately identifying shredded microplastics in water. The recently released Holography Micro-Plastic Dataset, obtained through the use of digital holography microscope techniques, offers the opportunity to test the capability of deep neural networks to distinguish between microplastics and other debris on a standard benchmark. The promising results obtained from the initial batch of experiments can be further improved by employing a combined approach involving different image mapping techniques and leveraging recent state-of-the-art deep learning models. Within this framework, we analyze various image fusion schemes to merge the paired dataset images (amplitude and phase grayscale images) into a single three-channel picture. We demonstrate that our proposed approach yields increased accuracy compared to both single-image data processing and other fusion techniques. Finally, the performance of our method is further enhanced by utilizing the DenseNet model as the backbone for deep learning-based microplastic classification.