High-frequency ultrasound combined with deep learning enables identification and size estimation of microplastics

Abstract Microplastics are widespread in aquatic and terrestrial environments, yet standard identification techniques remain slow, labor-intensive, and unsuitable for large-scale or in-situ monitoring. In this work, we investigate high-frequency ultrasound as a fast, non-destructive alternative for microplastic detection, material identification, and size estimation. A peak-based extraction method isolated particle-specific echoes, from which temporal and spectral features were computed. We evaluated several machine learning methods and introduced a one-dimensional convolutional neural network (1D-CNN) to classify material types. The proposed 1D-CNN achieved 99.75% accuracy, outperforming traditional models. Particle size was further estimated using material-specific multilayer perceptrons, which classified microspheres into four size ranges with up to 99.97% accuracy. These results show that high-frequency ultrasound encodes discriminative scattering patterns that can be learned directly from raw acoustic signals, offering a fast and scalable framework for microplastic characterization with potential for future real-time or in-situ applications.