# Semi-Supervised Method for Underwater Object Detection Algorithm Based on Improved YOLOv8
## 1 Introduction  
The ocean covers a vast area and contains a rich array of natural resources. The rational development and utilization of marine resources will become a focal point in the future as land resources continue to be developed and consumed. Underwater robotic technology has gained an increasingly significant role as a key tool for exploring and evaluating underwater biological resources. Underwater robots are able to reach extreme environments, such as the deep sea, which are difficult for humans to access and perform a variety of tasks. These robots can capture high-definition images and accurately detect targets through integrated high-definition cameras or sophisticated sonar systems. In the field of underwater object detection based on acoustic vision, there are significant limitations in detecting and preciselyidentifying objects at a close range because of the high noise characteristics and long wavelengths of underwater sound signals [1]. In contrast, images captured in the visible-light spectrum provide richer information, with more prominent details, and are more closely aligned with the human visual system. For the effective development and utilization of marine resources, the requisite accuracy of underwater object detection technology is critical. Existing underwater object detection algorithms can be broadly categorized into two subcategories: traditional methods and deep learning-based methods. Traditional methods rely on handcrafted feature extraction, which is not only time-consuming and expensive, but also limited in detection range. These methods often fail to capture the intricate features of objects effectively and perform particularly poorly when detecting small targets. Furthermore, the complexity of the underwater environment significantly limits its detection accuracy. With continuous advances in artificial intelligence (AI) and deep learning (DL) technologies, these methods have demonstrated outstanding capabilities in fields such as image recognition and object detection. Deep learning-based object detection algorithms can be broadly classified into two categories: two-stage object detection algorithms [2; 3; 4] and one-stage object detection algorithms [5; 6; 7]. Two-stage object detection algorithms generally offer higher detection performance, but they require more time for both training and inference, making them unsuitable for underwater object detection tasks. In contrast, one-stage object detection algorithms do not require a region proposal network like two-stage algorithms. Instead, they directly predict bounding boxes on the image, resulting in higher detection speed and lower computational cost, which makes them more suitable for real-time object detection requirements. In the field of underwater object detection, research on two-stage detectors has mainly focused on the Faster R-CNN model. [PERSON] et al. [8] proposed an underwater object detection algorithm based on Faster R-CNN and the Adversarial Occlusion Network (AON). This approach generates occlusion samples that are difficult to classify correctly during training, thus improving the robustness and generalization ability of the detection network in complex underwater environments. [PERSON] et al. [9] introduced a method combining Faster R-CNN and data augmentation techniques to address the challenges of human detection in underwater environments. By applying three types of data augmentation--image degradation, perspective transformation, and lighting variation--this method enhances the model's adaptability and detection accuracy under complex conditions. Due to the limitations of underwater hardware, most underwater object detection tasks are currently designed using single-stage detection methods. [PERSON] et al. [10] proposed a method for detecting uneaten feed pellets in aquaculture, based on an improved YOLO-V4 network, which addresses the issues of low contrast, small size, and the large number of feed pellets in underwater images. [PERSON] et al.