Detecting and quantifying microplastic particles have become important problems in environmental monitoring in recent years. In the natural environment, microplastic and nanoplastic particles are often mixed with large pieces of plastic, microalgae, microorganisms, and leaf fragments, etc., making them difficult to be distinguished. In addition, the microplastics themselves are made of different materials and have various shapes. As a result, the conventional classification methods based mostly on morphological characteristics cannot accurately classify microplastics in a complex environment, which brings great challenges to their detection and analysis. We have developed a classification and detection method based on digital holographic imaging and deep learning, which effectively classifies the types of microplastic particles by using the holographic interference fringe features of microplastic particles. With heterogeneous samples containing microplastic particles, microalgae and other substances, we are able to demonstrate the strength of our technique in the detection and characterization of the microplastics. Indeed, the results show that the deep learning network can automatically extract the features of holographic images of different particles in such samples, and delineate with good sensitivity the feature differences in the digital holograms that are caused by optical path differences introduced by various kinds of particles. Furthermore, this holographic feature-based classification is not affected by material morphological characteristics and has good robustness.
Water scattering is a significant limiting factor for underwater imaging quality. It changes the transportation direction of the original light path, causes the attenuation of light intensity, and so on. In this work, we use a synthetic polarizing camera to capture the images with different polarization states and reduce the impact of water scattering in one step with the underwater light propagation model and the Stokes vector. In addition, an untrained deep network is designed to complete the image descattering processing. Compared with the methods based on deep learning or physical model prior, it is more efficient. This technology is suitable for use in portable underwater imaging optical systems for real-time imaging and detecting particulate matter such as microplastics and microbial particles. It also broadens the application of underwater polarization imaging.
Microplastics, which are a major source of pollution in the ocean, need to be accurately detected and monitored. However, the current detection approaches often require complex optical instrumentation and a long time for image processing. Furthermore, because of the difficulties of particle sampling, it is hard to collect a dataset with sufficient images and a balanced distribution. Digital holography, which is a non-destructive imaging method, is suitable for the in situ imaging. In this work, we propose a novel digital holography microplastics classification system which combines deep learning and generative adversarial networks. We experimentally show that our method yields a higher accuracy for microplastics classification and can efficiently reduce the imbalance ratio of the dataset. This method can be modified for other in situ image classification tasks that likewise suffer from a small and imbalanced distribution dataset.
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