# Fish Detection in Fishways for Hydropower Stations Using Bidirectional Cross-Scale Feature Fusion
## 1 Introduction  
Recently, the wide construction of hydropower stations in river basins, including shuices and dams, has disrupted river continuity [1]. This has significantly impacted the habitats of aquatic species, and some species relying on upstream migration are facing the threat of extinction [2]. Fishways are designed to assist migratory fish in overcoming the abovementioned challenges, thereby ensuring the ecological balance within the river basin. Detecting aquatic life in migration channels is crucial for protecting the natural behavior of fish [3], and fish detection is a critical step [4]. However, the complexity of deployingmodels onsite in fishways is high, and images with turbid sediment are difficult to detect. To overcome these limitations, more efficient and accurate detection methods are needed. With the development of deep learning technology, significant progress has been made in object detection for vessels on the water. [PERSON] et al. [PERSON] et al. (2018) proposed a PEGNet detection model to address issues such as missed detections and false positives in dense watercraft scenarios, with experimental results showing its superior performance. [PERSON] et al. [PERSON] et al. (2018) introduced HO-ShipNet, a custom convolutional neural network (CNN), and experimental results demonstrated its excellent detection performance. This provides a solid foundation for fish detection in underwater environments and can be used to address many challenges associated with underwater images, including variations in lighting, water turbidity, and the diversity of marine species' appearances [PERSON] et al. (2018). [PERSON] et al. [PERSON] et al. (2018) enhanced the effectiveness of Faster R-CNN for detecting and recognizing marine organisms by utilizing data augmentation techniques. [PERSON] et al. [PERSON] et al. (2018) integrated Faster R-CNN with an adversarial occlusion network to achieve underwater object detection and overcome the challenges posed by occlusions in underwater environments. Similarly, [PERSON] et al. [PERSON] et al. (2018) improved underwater object detection accuracy by reweighting R-CNN samples based on errors from the Region Proposal Network in Boosting R-CNN. To enhance the performance of Faster R-CNN in fish detection, [PERSON] et al. [PERSON] et al. (2018) applied data augmentation techniques to improve underwater image quality. To focus on the morphological feature measurement of fish, [PERSON] et al. [PERSON] et al. (2018) designed the Mask_LaC R-CNN model, which offers stronger object segmentation capabilities compared to Faster R-CNN. [PERSON] et al. [PERSON] et al. (2018) developed Compositional FishNet for detecting fish based on low-quality underwater videos. [PERSON] et al. [PERSON] et al. (2018) used Mask R-CNN for automated detection and classification of southern African Roman seabreams. [PERSON] et al. [PERSON] et al. (2018) developed Faster R-CNN for shellfish recognition through an enhanced deep-learning framework, demonstrating the flexibility of Faster R-CNN in classifying various underwater organisms. [PERSON] et al. [PERSON] et al. (2018) and [PERSON] et al. [PERSON] et al. (2018) applied Faster R-CNN for seagrass detection, while [PERSON] et al. incorporated NASNet further to refine detection accuracy. [PERSON] et al. [PERSON] et al. (2018) integrated MSRCR with Mask R-CNN to recognize underwater creatures from small sample datasets. In addition to Faster R-CNN and Mask R-CNN, YOLO is also widely used to accomplish fish detection tasks. [PERSON] et al. [PERSON] et al. (2018) first proposed YOLO for real-time object detection, thereby activating the application for underwater object detection. [PERSON] et al. [PERSON] et al. (2018) adapted YOLO for real-time detection of underwater fish by incorporating transfer learning. [PERSON] et al. [PERSON] et al. (2018) utilized YOLO v4 to study fish locomotion in aquaponic systems. In addition, [PERSON] et al. [PERSON] et al.