# Fish Detection in Fishways for Hydropower Stations Using Bidirectional Cross-Scale Feature Fusion
## 5 Conclusions and Future Work  
This study addresses the high cost, low efficiency, and accuracy issues associated with fish detecting in the fishway. By selecting a portion of the Fish4 knowledge and images of various fish species collected in a laboratory environment, a Fish26 dataset was created, which contains 26 species of fish. To address the challenges of image blurring due to turbid water and image degradation from insufficient lighting in underwater environments, the fish detection algorithm was proposed, effectively performing fish detection tasks in fishway environments.  
Through comparison experiments with loss functions, it was found that the WIoU loss function used in the fishway fish detection algorithm proposed in this paper achieved a [EMAIL_ADDRESS] value of 91.8%, with a significant improvement in accuracy. Compared with state-of-the-art methods, the improved model showed a 1.7% increase in detection accuracy, a 23.4% reduction in computational complexity, and a 24% reduction in model parameters. This resulted in enhanced detection accuracy, a lighter model, and fewer missed and false detections. Additionally, we conducted ablation experiments on the proposed algorithm to verify its high accuracy and robustness. Field tests were also carried out in the fishway near a specific hydropower station. The results show that the fishway fish detection algorithm proposed in this paper not only performs well in a laboratory environment but also meets the requirements for fishway fish detection in real-world applications. However, due to the varying fish species in different fishways across seasons, the algorithm proposed in this paper is currently only applicable to the specific fishway during the given season. Fish detection in other fishways still faces challenges. Future work will focus on expanding fishway fish datasets for different fishways and conducting diversified experiments across multiple fishways in various seasons to enhance the algorithm's robustness, as well as exploring algorithms with higher detection accuracy and real-time performance. Additionally, the generalizability of the algorithm for detecting other underwater targets remains an area for further research.  
J.W.: conceptualization, methodology, project administration; Y.G.: methodology, formal analysis, writing--original draft; W.D.: data curation, writing--review and editing, supervision; E.L.: supervision, funding acquisition; X.H.: writing--review and editing, supervision; D.Z.: supervision, funding acquisition. All authors have read and agreed to the published version of the manuscript.  
Figure 10: The proposed fishway fish detection model can perform real-time monitoring of different fish species in real fishways.  
**Funding:** This work was supported by the research on high-precision industrial vision measurement methods for large components (Wuhan Science and Technology Bureau Shuguang Program, No. 2023010201020374) and the research on calibration methods for distributed visual perception systems of intelligent agricultural machinery clusters in hilly and mountainous areas (Hubei Province Natural Science Foundation Youth Program, No. 2024 AFB501).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article; future inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.