Resumen
In aquaculture, the accurate recognition of fish underwater has outstanding academic value and economic benefits for scientifically guiding aquaculture production, which assists in the analysis of aquaculture programs and studies of fish behavior. However, the underwater environment is complex and affected by lighting, water quality, and the mutual obscuration of fish bodies. Therefore, underwater fish images are not very clear, which restricts the recognition accuracy of underwater targets. This paper proposes an improved YOLO-V7 model for the identification of Takifugu rubripes. Its specific implementation methods are as follows: (1) The feature extraction capability of the original network is improved by adding a sizeable convolutional kernel model into the backbone network. (2) Through ameliorating the original detection head, the information flow forms a cascade effect to effectively solve the multi-scale problems and inadequate information extraction of small targets. (3) Finally, this paper appropriately prunes the network to reduce the total computation of the model; meanwhile, it ensures the precision of the detection. The experimental results show that the detection accuracy of the improved YOLO-V7 model is better than that of the original. The average precision improved from 87.79% to 92.86% (when the intersection over union was 0.5), with an increase of 5.07%. Additionally, the amount of computation was reduced by approximately 35%. This shows that the detection precision of the proposed network model was higher than that for the original model, which can provide a reference for the intelligent aquaculture of fishes.