# Optimization and Application of Improved YOLOv9s-UI for Underwater Object Detection
## 1 Introduction  
Object detection technology plays a crucial role in various contemporary technological domains, including autonomous driving, smart surveillance, and medical image analysis, where both high real-time performance and accuracy are indispensable. The You Only Look Once (YOLO) series of models [1; 2], well known for their efficient processing capabilities, are extensively used in the object field and are commonly applied in diverse practical scenarios. However, object detection encounters numerous challenges in underwater environments. Factors such as inadequate lighting, image quality deterioration due to water scattering and refraction, low contrast, and visual disturbances significantly undermine the efficacy of conventional object detection technologies [3; 4]. Traditional methods for underwater object detection primarily depend on feature extraction and image processing techniques to identify and locate targets [5; 6]. These approaches involve utilizing edge detection, shape matching, and optical flow techniques for both dynamic and static elements in images and videos [7; 8]. However, these methods are often sensitive to environmental conditions, especially in complex underwater environments characterized by fluctuating conditions and murky water quality, leading to a decrease in detection accuracy. Furthermore, these conventional techniques are computationally intensive and exhibit poor real-time performance, rendering them unsuitable for applications that necessitate quick responses [9; 10]. Consequently, these constraintssignificantly diminish the practical utility and dependability of traditional underwater object detection methods in operational scenarios. In complex underwater environments, increasing the receptive field helps to better capture the contextual information and environmental characteristics of the target, thereby improving the accuracy of prediction. Deep convolutional neural networks (CNNs) have shown excellent processing capabilities in adjusting network depth and convolution kernel size to expand the receptive field [11]. In addition, the adoption of nonlinear activation functions, the integration of attention mechanisms, and the application of data enhancement technology [12] can significantly improve the network's ability to process input data. Technologies such as feature pyramid network [13], multi-scale fusion module [11], and Atrous Spatial Pyramid Pooling (ASPP) [14] can effectively integrate feature information at different scales and enhance the recognition ability of the system. Advanced architectures such as the standard Transformer and its variants [2; 7] and DenseNet [15] further improve model performance and adaptability by managing complex data structures. In the domain of underwater object detection, deep learning technologies have garnered significant attention [10; 16]. Researchers have started to employ deep neural network methodologies in this field [17; 18]. While YOLO algorithms have shown remarkable speed and accuracy in numerous applications, their performance necessitates improvement when confronted with the distinctive challenges of underwater settings [19; 20]. This is significant in the detection of small or densely clustered underwater objects. Therefore, specific optimizations are essential to improve the effectiveness and robustness of the YOLO algorithm in such extreme conditions [21; 22]. [PERSON] et al. [23] proposed a cascaded model based on the User-Generated Content (UGC)-YOLO network structure, which improves underwater target detection by integrating global semantic information between two residual stages of the feature extraction network. However, experimental results revealed only modest improvements over the baseline network. [PERSON] et al. [24] combined MobileNetv2 and YOLOv4 to achieve a balance between detection efficiency and accuracy, significantly enhancing the recognition of small targets through a lightweight structure and multi-scale attention feature integration.