PPCL-RSE: Point prediction for counting and localization of litopenaeus vannamei fry with region-based super-resolution enhancement

•An efficient method based on point for shrimp fry counting and locating is proposed.•The method demonstrates improved and more stable counting and locating efficiency, even in the presence of background noise and high density.•This method surpasses the current crowd counting methods in terms of accuracy for both counting and locating shrimp fry. Accurate locating and counting of litopenaeus vannamei fry can provide substantial support for vannamei fry sales and scientific feeding. However, traditional methods not only require visual observation by experts, but also are time-consuming and labor-intensive, with no guarantee to reach consensus between salesmen and customers. In contrast, more innovative methods require more expensive equipment or are only effective under specific conditions. The small size and high density nature of the shrimp fry makes its counting even more challenging. In this study, a point prediction method for counting and localization of litopenaeus vannamei fry with region-based super-resolution enhancement (PPCL-RSE) is proposed. Through the inclusion of three modules of density partitioning, high-density region expansion and regional super-resolution, the accuracy of fry counting and locating is improved. The model is deployed on a cloud server for convenient fry counting and localization based on images taken by smartphone cameras. To achieve this, we create a dataset called Vannamei-983 which contains images with more than 1,000,000 fry labeled. The proposed method shows accuracies of 99.04 % and 97.71 % in counting and localization of shrimp fry in low- and high-density images, respectively. The excellent model performance also demonstrate the effectiveness of the strategies considered in the study.