Fish Catch Sorting and Detection Model Improved Based on YOLOv8 Model

Abstract

The primary challenge in trawl fishing lies in its limited selectivity, resulting in highly diverse fish catches with severe mixed-species issues. The catches from trawl fishing require manual sorting, leading to low work efficiency and high labor demands. In order to tackle this issue, the present study introduces an enhanced version of the DWR module by utilizing DilatedReparamBlock convolution, introducing a novel dilated convolution module. This module is integrated into the YOLOv8 model, enhancing its capacity to capture features from the enlarged receptive field in the upper network layers. Furthermore, a new attention mechanism based on a multi-branch structure with the CA attention mechanism is incorporated into the YOLOv8 model. This attention mechanism fully extracts image features, enhances feature representation, strengthens the generation of offsets and sampling weights, and improves the accuracy of target recognition. Lightweight improvements to the detection head are achieved through the use of shared convolutions, ultimately resulting in a significant reduction in the number of model parameters. Our empirical findings indicate that the refined model exhibits a 2.2% enhancement in mAP@0.5 when benchmarked against the initial YOLOv8 model, Offering a significant reference for the advancement of an effective embedded system for fish sorting.