Research on fish bait particles counting model based on improved MCNN

Abstract

• A model of fish bait particle counting based on improved MCNN network is proposed. • The method based on density map is first introduced to the study of counting fish bait particles. • A network suitable for feature extraction of small targets is provided. • A model fused with transposed convolutional layer can significantly generate higher-quality density maps. In intensive aquaculture, quantitative research on bait particles has important theoretical and practical significance for reducing breeding costs and realizing intelligent feeding, and can effectively reduce environmental pollution caused by excessive bait. However, the current methods based on acoustics and machine vision are difficult to count tiny or overlapped bait particles, resulting in low counting accuracy of model. In order to solve the above problems, this paper proposes a counting model for small bait particles, using MCNN as the basic network to achieve efficient and stable counting performance in aquaculture. The proposed model consists of two parts: a multi-column feature extractor and a density map generator. The multi-column feature extractor is used as the front-end network of model. By introducing multiple branches, convolution kernels with different size are used to extract feature information of different scales and adapt to the non-uniform bait particles. Thereby, the multi-scale problem caused by perspective variation of bait particles is solved. In addition, the density map generator is used as the back-end network of model. Instead of the 1 × 1 convolutional layer in the original MCNN, the density map generator takes advantage of transposed convolutional layer to restore the poor quality of the density map caused by the down-sample in the multi-column feature extraction network. Besides, the ReLU function is helpful to increase the fitting ability of the model. Moreover, in order to further improve the quality of the density map and the counting performance of the model, the Euclidean distance loss and the structural similarity loss are combined as the final loss function of the model. The fusion of loss is beneficial to solve the ambiguity of density map caused by the Euclidean loss function. As the experimental results showed, compared with the original MCNN network, the mean absolute error MAE of the proposed model is reduced by 69.35%, and the mean square error MSE is reduced by 70.87%. In summary, the proposed bait particles counting model has high counting accuracy and is more stable, it is in favor of providing theoretical support for intelligent feeding study in aquaculture.