SLUDGE MICROSCOPIC IMAGE CHUNKING BASED ON DEEPLABV3+ METHOD

Activated sludge phase contrast microscopic image segmentation based on traditional image methods often suffers from over-segmentation, under-segmentation, and even segmentation failure. An improved DeepLabV3+ network is used for block segmentation to improve the segmentation effect of filamentous bacteria. This method divided a high-resolution phase-contrast microscopy image into multiple small areas with a certain overlap rate. The segmented images were stitched back to the original resolution. The proposed method was verified on the microscopic image data of activated sludge from a municipal sewage treatment plant. The experimental results show that the lightweight segmentation strategy model has a certain degree of improvement in segmentation accuracy, recall, pixel accuracy and IoU performance indicators compared with the undivided DeepLabV3+, U-Net, and SegNet models, and the model size is significantly reduced.