Improved YOLOV7-TINY Network for Sea Bream Detection
DOI:
https://doi.org/10.54097/c6kqnn36Keywords:
Cascaded feature fusion, Attention mechanism, Improved YOLOV7-TINY network, ECIOUAbstract
Accurate identification of underwater fish species is of great scientific and economic significance in aquaculture, as it can provide scientific basis for aquaculture production and promote related research. However, the complexity of the underwater environment is affected by various factors such as light, water quality, and mutual occlusion of fish species. Therefore, underwater fish images are often not clear enough, which limits the accurate identification of underwater targets. In this paper, an improved YOLOV7-TINY model for sea bream detection is proposed. We employ FasterNet to replace the backbone network of the YOLOV7-TINY model, further reducing model parameters and computational complexity without compromising accuracy. By leveraging cascaded feature fusion in the backbone network, we effectively address the challenges posed by multi-scale datasets and insufficient information extraction. Additionally, the RESNETCBAM attention mechanism is incorporated into the feature maps at three different scales, allowing the network to better capture relevant information from complex underwater environments while minimizing unnecessary interference. Finally, the ECIOU loss function is adopted to optimize frame adjustments and reduce the training time of the model.
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