A fish image segmentation methodology in aquaculture environment based on multi-feature fusion model

Abstract

Underwater fish image processing is one of the key technologies to realize intelligent aquaculture. However, due to the complexity of marine environments, underwater fish images usually have the characteristics of color cast, unbalanced contrast, and blur. Current underwater fish image segmentation methods lack adaptive models and have low segmentation accuracy. Hence, this paper constructs a convolutional neural network-based image segmentation model. This paper first proposes a fish image preprocessing method based on pixel threshold segmentation. To enhance the important features in a fish image, this method uses the minimum Euclidean distance between the original image peaks to redefine the threshold and fuses the thresholded image with the original image. Second, to strengthen the extraction of high-level semantic features of images, the multiscale attentional feature extraction module (MAFEM), which fuses the adaptive channel attention mechanism with the hybrid dilated convolutional pyramid pooling module, is proposed. In this paper, a data set in voc format is produced based on underwater fish images, and this data set is used to verify the model in this paper. The mean intersection over union (MIoU) reaches 92.6%. Compared with other traditional segmentation models, the MIoU, mean pixel accuracy (MPA), and balanced F score (F1-score) of the segmentation results of the model in this paper are increased by averages of 1.84%, 0.785%, and 1.18%, respectively. The experimental results show that this model has a better segmentation effect than other models and provides a theoretical basis for intelligent monitoring of underwater fish body length measurement, weight estimation, and discrimination of growth and health statuses.