# A Method for Underwater Biological Detection Based on Improved YOLOXs
## 1 Introduction  
With the rapid development of social and economic activities, global issues such as the scarcity of terrestrial resources and environmental degradation have become increasingly prominent. In pursuit of sustainable human development, the exploration and utilization of marine resources have become a focal point of attention. Oceans cover the vast majority of the Earth's surface and harbor abundant resources, including petroleum, natural gas, minerals, and fisheries [1]. At the same time, the oceans harbor diverse ecosystems and serve as habitats for numerous species. Therefore, the proper development and utilization of marine resources can not only meet human demands for energy, food, and raw materials but also protect the integrity of marine ecosystems and ensure sustainable development. In this context, many coastal countries are actively engaged in the exploration and exploitation of marine resources. Through technological innovation and cooperation, they are seeking and developing deep-sea oil and gas resources, conducting exploration and exploitation of seabed minerals, and promoting the sustainable development of fisheries. In the process of marine resource development and utilization, underwater target detection is a crucial area of research in marine technology. Traditional underwater target detection techniques primarily involve manually designed feature extraction from images [2], followed by the use of machine learning algorithms for target classification [3]. While this approach can achieve underwater target detection to some extent, it has drawbacks in the feature extraction stage. Manual feature extraction incurs significant laborand time costs, and the features that are extracted manually may have limitations, such as being relatively simple and lacking strong generalization capabilities. To address these challenges, there is a need for advanced methods that leverage modern computer vision and deep learning techniques to enhance underwater target detection. These approaches can automate the feature extraction process, reducing human intervention and improving the overall efficiency and effectiveness of underwater target detection in complex marine environments. To address these challenges, underwater target detection techniques based on deep learning have emerged [4; 5]. Deep learning-based methods leverage deep neural networks to automatically learn features, including more abstract and advanced feature representations. Through extensive training with large amounts of data, these models acquire better generalization capabilities, enabling end-to-end underwater target detection. In recent years, deep learning-based underwater target detection techniques have made significant progress and can be categorized into two-stage and one-stage target detection algorithms. Two-stage target detection algorithms first generate a series of candidate boxes as samples and then use convolutional neural networks for sample classification. Examples of two-stage algorithms include R-CNN [6], FastR-CNN [7], FasterRCNN [8], and Cascade R-CNN [9], among others. On the other hand, one-stage target detection algorithms do not need to generate candidate boxes; they only need to input the image once to predict all the bounding boxes and classifications. This approach is characterized by its simple structure and fast processing speed. Examples of one-stage algorithms include SSD [10] and the YOLO (You Only Look Once) series [11; 12; 13; 14; 15]. With increasing underwater exploration activities, more scholars are applying target detection techniques to underwater environments. In 2019, [PERSON] et al. applied FastRCNN to underwater seagrass target detection [16]. They used data augmentation to generate more diverse training samples and employed transfer learning techniques to accelerate the training process and improve the detection performance. In 2020, [PERSON] et al. utilized the FastRCNN model and temporal information to detect and classify fish in underwater environments [17].