Advancing microplastic surveillance through photoacoustic imaging and deep learning techniques

Abstract

Microplastic contamination presents a significant global environmental threat, yet scientific understanding of its morphological distribution within ecosystems remains limited. This study introduces a pioneering method for comprehensive microplastic assessment and environmental monitoring, integrating photoacoustic imaging and advanced deep learning techniques. Rigorous curation of diverse microplastic datasets enhances model training, yielding a high-resolution imaging dataset focused on shape-based discrimination. The introduction of the Vector-Quantized Variational Auto Encoder (VQVAE2) deep learning model signifies a substantial advancement, demonstrating exceptional proficiency in image dimensionality reduction and clustering. Furthermore, the utilization of Vector Quantization Microplastic Photoacoustic imaging (VQMPA) with a proxy task before decoding enhances feature extraction, enabling simultaneous microplastic analysis and discrimination. Despite inherent limitations, this study lays a robust foundation for future research, suggesting avenues for enhancing microplastic identification precision through expanded sample sizes and complementary methodologies like spectroscopy. In conclusion, this innovative approach not only advances microplastic monitoring but also provides valuable insights for future environmental investigations, highlighting the potential of photoacoustic imaging and deep learning in bolstering sustainable environmental monitoring efforts.