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Underwater in-situ darkfield microscopy imaging can grasp excellent resolution and high contrast images for observing transparent and living plankton. However, its limited field of view and shallow depth of field leads to a large amount of redundant data during image acquisition. Manual extraction of regions of interest (ROIs) imposes significant labor and time costs. Therefore, we develop an automatic ROI extraction method to obtain high-quality in-focus individual plankton images from raw images. A multiscale underwater in-situ microscopy system (MUIMS)based on darkfield imaging has been established. The system allows for the monitoring of plankton in the size range from 10μm to 1cm. A series of preprocessing steps are performed on the raw darkfield images to make edge detection and segmentation of plankton regions. This can realize rapid extraction of ROIs from the raw images. A dataset is constructed from these ROIs, and each image is labeled to indicate whether it is a high-quality image. By using a transfer learning strategy, we improve a pre-trained ResNet-18 model and train it to accurately classify high-quality ROIs. The experimental results show that this method can rapidly extract useful information from raw images, achieving an accuracy of over 90% in judging the quality of ROIs. This research is beneficial for building a dataset of high-quality plankton, which provides a crucial data foundation for ecological research and marine biodiversity conservation.
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