Improved weighted bidirectional FPN aquatic real-time target detection model based on cross-scale connections

As a pillar industry in coastal areas, aquaculture needs artificial intelligence technology to drive its economic development. This paper proposes a new method of multi-scale feature fusion and integrates it into the YOLOv5 backbone network for automated operations in the aquaculture industry. This model completes the computerized classification and detection of aquatic products, increases the industry's productivity, and fosters economic development. To provide a foundation of data for training the model, this research creates a dataset comprising 15 species of marine products. The data preprocessing section suggests an underwater image enhancement approach to raise the dataset's quality. Mosaic data augmentation is presented to enrich the dataset and bolster its features. A weighted bi-directional feature pyramid network is improved and fused into the necking network to improve the ability of multi-scale feature fusion of the network, significantly strengthening the efficiency of feature extraction of the network. Moreover, the accuracy and speed of model prediction are significantly increased by integrating the SimAM attention mechanism and introducing the FReLU activation function in the network backbone section. The comparison and ablation experiments show the suggested model's superiority and efficacy. The enhanced YOLOv5 target detection model's experimental results, verified by the mAP and FPS evaluation metrics, can achieve 0.953 and 203 frames per second. Compared to the base YOLOv5 network, the evaluation metrics improved by 0.067 and 48 frames per second, respectively. In summary, our method can quickly and accurately identify aquatic products and achieve real-time target detection of marine products, laying the foundation for developing automation systems in the aquaculture industry.