Fish Detection in Fishways for Hydropower Stations Using Bidirectional Cross-Scale Feature Fusion

Fishways can effectively validate the effectiveness and rationality of their construction, optimize operational modes, and achieve intelligent scientific management through fish species detection. Traditional fish species detection methods for fishways are unsuitable due to inefficiency and disruption of the fish ecological environment. Therefore, combining cameras with target detection technology provides a better solution. However, challenges include the limited computational power of onsite equipment, the complexity of model deployment, low detection accuracy, and slow detection speed, all of which are significant obstacles. This paper proposes a fish detection model for accurate and efficient fish detection. Firstly, the backbone network integrates FasterNet-Block, C2f, and an efficient multi-scale EMA attention mechanism to address attention dispersion problems during feature extraction, delivering real-time object detection across different scales. Secondly, the Neck introduces a novel architecture to enhance feature fusion by integrating the RepBlock and BiFusion modules. Finally, the performance of the fish detection model is demonstrated based on the Fish26 dataset, in which the detection accuracy, computational cost, and parameter count are significantly optimized by 1.7%, 23.4%, and 24%, respectively, compared to the state-of-the-art model. At the same time, we installed detection devices in a specific fishway and deployed the proposed method within these devices. We collected data on four fish species passing through the fishway to create a dataset and train the model. The results of the practical application demonstrated superior fish detection capabilities, with rapid detection ability achieved while minimizing resource usage. This validated the effectiveness of the proposed method for equipment deployment in real-world engineering environments. This marks a shift from traditional manual detection to intelligent fish species detection in fishways, promoting water resource utilization and the protection of fish ecological environments. © 2025 by the authors.