## 5 Conclusions  
The proposed enhancements in Cycle GAN and YOLOv5-BiFC effectively address the issues of sparse image data and low recognition efficiency in large deep learning models. The Cycle GAN improved with self-attention and residual modules expands the dataset of sewage outlets under nighttime conditions. This augmentation significantly enhances the diversity of the target detection background and strengthens the model's generalization ability. The YOLOv5-BiFC model is introduced to efficiently learn the main information features of sewage outlets and discharge during the model training phase. The post-training model size is reduced to 7.8M, a 44.2% reduction compared to the original YOLOv5 model, achieving an FPS of 26.7, laying the foundation for real-time monitoring of sewage outlets. The use of the enhanced network demonstrates improvements in mAP and F1 compared to the original YOLOv5 network, proving its effectiveness in reducing model size and recognition efficiency. When compared to other models such as SSD, Faster-RCNN, YOLO-MobileNetV3, and YOLOv5, YOLOv5-BiFC exhibits advantages in both model accuracy and efficiency. This confirms that the model trained with YOLOv5-BiFC can achieve accurate, fast, and intelligent detection of sewage outlets in practice.  
The improved Cycle GAN has successfully addressed data scarcity issues and YOLOv5-BiFC has enhanced the model's recognition efficiency. However, factors such as the computational power of neural networks, training time costs, and the generation of higher-quality images cannot be overlooked. In the realm of image generation, it is deemed necessary to explore more complex and effective modules, such as superior attention mechanisms or exceptional neural network layers, to enhance the ability to extract features of interest regions, thereby improving the quality and variety of generated image data. Regarding model training for recognition, we will focus on training costs and recognition accuracy. Without compromising recognition efficiency, further research will be conducted to strike a balance between the two, aiming to achieve optimal levels of training costs and recognition accuracy. Additionally, special cases may arise during the identification of discharge outlets, such as semi-exposed and non-exposed sewage identification, and the discharge of white liquid to determine whether the sewage is reasonable. In future studies, alternative visual methods will be employed to analyze and determine special discharge situations, enhancing the credibility of the results and strengthening the model's generalization capabilities.  
Methodology, C.L., Y.J. and M.Y.; Formal analysis, M.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by Beijing Science and technology planning project under Grant Z201100001820022 and Multiscale prediction technology and platform for upland lake upstream water inflow: A case study of Serinco Basin under Grant number WR110145B0112024.  
Not applicable.  
The dataset used is in the public domain. If necessary, please contact our corresponding author.  
The authors declare no conflicts of interest.  
Figure 16: Heat map comparison of different improved models.