Automatic Penaeus Monodon Larvae Counting via Equal Keypoint Regression with Smartphones

Abstract

Today, large-scale Penaeus monodon farms no longer incubate eggs but instead purchase larvae from large-scale hatcheries for rearing. The accurate counting of tens of thousands of larvae in these transactions is a challenging task due to the small size of the larvae and the highly congested scenes. To address this issue, we present the Penaeus Larvae Counting Strategy (PLCS), a simple and efficient method for counting Penaeus monodon larvae that only requires a smartphone to capture images without the need for any additional equipment. Our approach treats two different types of keypoints as equip keypoints based on keypoint regression to determine the number of shrimp larvae in the image. We constructed a high-resolution image dataset named Penaeus_1k using images captured by five smartphones. This dataset contains 1420 images of Penaeus monodon larvae and includes general annotations for three keypoints, making it suitable for density map counting, keypoint regression, and other methods. The effectiveness of the proposed method was evaluated on a real Penaeus monodon larvae dataset. The average accuracy of 720 images with seven different density groups in the test dataset was 93.79%, outperforming the classical density map algorithm and demonstrating the efficacy of the PLCS.

Deconvolution Enhancement Keypoint Network for Efficient Fish Fry Counting

“…In the first step, we launched experiments on the FishFry-2023 dataset and obtained the results produced by DEKNet. In the second step, to verify the universality of DEKNet, we conducted experiments on the existing Penaeus dataset [26] and a publicly available Adipocyte Cells dataset [27]. Finally, subsequent ablation experiments were carried out on the FishFry-2023 dataset to verify the performance of each component in DEKNet.…”

“…Consequently, when the heatmap resolution matches that of the input image, the localization accuracy improves significantly, resulting in fewer errors [20]. Furthermore, DEKNet can be used to count the penaeus [26] and the cells [27]. Therefore, DEKNet is more robust compared to existing methods in the research field of fish fry counting.…”