Version 1
: Received: 6 August 2024 / Approved: 7 August 2024 / Online: 7 August 2024 (11:34:05 CEST)
How to cite:
Xie, Y.; Xiang, J.; Li, X.; Chen, Y. An Intelligent Fishery Detection Method Based on Cross-Domain Image Feature Fusion. Preprints2024, 2024080476. https://doi.org/10.20944/preprints202408.0476.v1
Xie, Y.; Xiang, J.; Li, X.; Chen, Y. An Intelligent Fishery Detection Method Based on Cross-Domain Image Feature Fusion. Preprints 2024, 2024080476. https://doi.org/10.20944/preprints202408.0476.v1
Xie, Y.; Xiang, J.; Li, X.; Chen, Y. An Intelligent Fishery Detection Method Based on Cross-Domain Image Feature Fusion. Preprints2024, 2024080476. https://doi.org/10.20944/preprints202408.0476.v1
APA Style
Xie, Y., Xiang, J., Li, X., & Chen, Y. (2024). An Intelligent Fishery Detection Method Based on Cross-Domain Image Feature Fusion. Preprints. https://doi.org/10.20944/preprints202408.0476.v1
Chicago/Turabian Style
Xie, Y., Xiaoyong Li and Yang Chen. 2024 "An Intelligent Fishery Detection Method Based on Cross-Domain Image Feature Fusion" Preprints. https://doi.org/10.20944/preprints202408.0476.v1
Abstract
Target detection technology plays a crucial role in fishery ecological monitoring, fishery diversity research, and intelligent aquaculture. Deep learning, with its distinct advantages, provides significant convenience to the fishery industry. However, it still faces various challenges in practical applications, such as significant differences in image species and image blurring. To address these issues, this study proposes a multi-scale, multi-level, and multi-stage cross-domain feature fusion model. In order to train the model more effectively, a new dataset called Fish52 (Multi-scene Fish dataset) was constructed, on which the model achieved an mAP of 82.57%. Furthermore, we compared prevalent one-stage and two-stage detection methods on the Lahatan (single-scene fish dataset) and Fish30 dataset and tested them on the F4k and FishNet dataset. The mAP of our proposed model on the Fish30, Lahatan, F4k, and FishNet datasets reaches 91.72%, 98.7%, 88.6%,and 81.5% respectively, outperforming existing mainstream models. Comprehensive empirical analysis indicates that our model possesses high generalization ability and has reached advanced performance levels. In this study, the depth of the model backbone is deepened, a novel neck structure is proposed, and anew module is embedded therein. To enhance the fusion ability of the model, a new attention mechanism module is introduced. In addition, in the adaptive decoupling detection header module, introducing classes with independent parameter sand regression adapters reduces interaction between different tasks. The proposed model can better monitor fishery resources and enhance aquaculture efficiency. It not only provides an effective approach for fish detection but also has certain reference significance for the identification of similar target sin other environments, and offers assistance for the construction of smart fisheries and digital fisheries.
Keywords
Fish detection; Multi-scale feature fusion; Decoupled detection head; Reinforcement feature
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
