Shrimp egg counting with fully convolutional regression network and generative adversarial network

Abstract

Abstract Accurate egg counting is the basic demand in the hatcheries of the aquaculture. However, the time-consuming, fallible manual counting is now still adopted in most situations. Also, many traditional automatic egg counting methods cannot provide enough high efficiency and accuracy, especially in densely-distributed cases. In this paper, we propose a novel convolutional neural network (CNN) based method for shrimp egg counting. Compared to traditional methods mainly based on contour detection or image segmentation, the proposed method exploits the density map regression and is more efficient in densely-distributed case even with severe occlusion. Firstly, a new dataset of the redclaw crayfish Cherax quadricarinatus eggs is collected, which includes 450 images with about 272,000 eggs annotated accurately. Also, a synthetic dataset generation method based on generative adversarial network (GAN) is proposed to avoid the onerous manual labeling, and has potential applications in the supervised learning for the densely-distributed eggs or larvae counting. Then, considering the advantages of no manual preprocessing and fine-grained feature extraction in CNNs, a shrimp egg counting network (SECNet) based on fully convolutional regression network (FCRN) is proposed to realize the counting through regressing the input image into its density map. The test results show the average counting accuracy of the proposed SECNet can be up to 99.2 % when the SECNet is pre-trained on the synthetic dataset and finetuned on the collected dataset. Finally, a simple and cheap computer vision based counting setup is built by using three off-the-shelf devices and a convenient operation program integrated with the SECNet is developed for a person computer, which provides an accurate, real-time, and highly-efficient egg counting way.