## 1 Introduction  
With the development of urbanization and industrialization, although some progress has been made in the treatment of water pollution, there are still challenges associated with its management. Traditional technologies are no longer sufficient to meet modern demands, and there is an urgent need to develop new intelligent technologies for water pollution monitoring and control. Deep learning object detection algorithms process camera video feeds through parallel computing, extracting image features in real time. In particular, these algorithms have shown significant effectiveness in addressing complex water pollution issues arising from industrial, urban, and agricultural non-point sources. The concept of Artificial Intelligence (AI) was introduced by the American scientist [PERSON] in 1956. It is a technology that simulates, extends, and expands human intelligence, capable of processing various data such as visual images and sound and reacting accordingly [1]. Since the 1990s, machine learning has become mainstream AI research and has been widely applied in fields such as agriculture, climate, finance, and security [2, 3, 4]. AI is considered to be an efficient alternative to traditional programs and mathematical algorithms, gradually becoming a focal point in environmental governance. Studies have shown that AI is extensively used in water pollution [5], air pollution [6], solid waste treatment [7], climate change [8], and other environmental areas [9], serving as a crucial tool in environmental regulation. The types of AI models used in water environmental governance include Artificial Neural Networks (ANNs), Support Vector Machines (SVM), and Genetic Algorithms (GA), among others. Currently, deep learning object detection algorithms such as SSD [10], the YOLO series [11; 12; 13], and the RCNN series [14; 15] are widely used in various recognition tasks. Studies have shown that deep learning algorithms perform well in complex environments and can robustly detect objects. By integrating or modifying classifiers such as DenseNet [16], EfficientNet [17], and detectors like YOLOv5, YOLOv7 [18], YOLOv8 [19], YOLOR [20], and other algorithm applications [21], the accuracy of the model for detecting sewage outlet pollution can be enhanced. Deep learning relies on the neural network structure and data quality. Small datasets can lead to model overfitting, affecting generalization ability. Data augmentation (DA) is one of the standard methods to address overfitting. Generative Adversarial Networks (GANs) create synthetic data instances using two neural networks and have been used for tasks such as super-resolution and image translation [22]. GANs, in cases with limited data, enhance model performance by generating high-quality images. For instance, GANs have been employed to enhance the performance of CNN models in detecting datasets related to COVID-19 infections [23]. Most object detection research relies on high-quality training datasets. Cycle GAN, with its low initial data requirements, is suitable for enhancing emission data for exhaust outlets through mutual transformation and data expansion. There are several commonly used public datasets for traditional object detection, such as VOC2007 [24], COCO [25], and Global Wheat [26]. However, there is currently no publicly available dataset for sewage outfall pollution. Sewage pollution data differ from traditional datasets in the following ways:  
1. The discharge outlets in the images captured by the camera equipment exhibit multi-scale characteristics, posing a certain challenge to their recognition. 2. The environment surrounding the sewage outlets is complex and diverse, with challenges such as vegetation obstruction posing significant obstacles to the detection of the sewage outlets. 3. In contrast to common targets such as vehicles and animals, the spatial distribution of emission outlets exhibits sparsity and non-uniformity.