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This paper proposes a novel pixel interval down-sampling network (PID-Net)for dense tiny object (yeast cells) counting tasks with higher accuracy. ThePID-Net is an end-to-end convolutional neural network (CNN) model with anencoder--decoder architecture. The pixel interval down-sampling operations areconcatenated with max-pooling operations to combine the sparse and densefeatures. This addresses the limitation of contour conglutination of denseobjects while counting. The evaluation was conducted using classicalsegmentation metrics (the Dice, Jaccard and Hausdorff distance) as well ascounting metrics. The experimental results show that the proposed PID-Net hadthe best performance and potential for dense tiny object counting tasks, whichachieved 96.97\% counting accuracy on the dataset with 2448 yeast cell images.By comparing with the state-of-the-art approaches, such as Attention U-Net,Swin U-Net and Trans U-Net, the proposed PID-Net can segment dense tiny objectswith clearer boundaries and fewer incorrect debris, which shows the greatpotential of PID-Net in the task of accurate counting.

An application of Pixel Interval Down-sampling (PID) for dense tiny microorganism counting on environmental microorganism images