# Semi-Supervised Method for Underwater Object Detection Algorithm Based on Improved YOLOv8
## 1 Introduction
[11] employed a fuzzy network to process the dataset and made an improvement to YOLOv4 by incorporating an attention module, which enhanced the feature extraction ability of the backbone network. Furthermore, [PERSON] et al. [12] introduced an improved YOLOv5 object detection framework that significantly enhanced the accuracy and robustness of underwater organism detection by integrating the BoT3 self-attention mechanism and the MLLE image enhancement method. [PERSON] et al. [13] presented an enhanced algorithm based on YOLOv8 designed to address the challenges of poor image quality and suboptimal recognition accuracy in robotic underwater object detection. The proposed enhancements encompass the incorporation of Adaptive Deformable Convolution (DCNv3) and Spatial Pyramid Pooling with Cross-Stage Partial Connections (SPPFCSPC), as well as the utilization of the Weighted Intersection over Union (WIoU) loss function to refine detection performance. [PERSON] et al. [14] enhanced the YOLOv8 model by integrating Compact Inverted Blocks (CIB), the PSA self-attention mechanism, and a multi-scale feature fusion technique grounded in Gold-YOLO. These innovations significantly augment the precision and robustness of object recognition in intricate and challenging underwater environments. The aforementioned studies have improved algorithms from different perspectives and achieved certain results. However, underwater object detection still faces several challenges. First, the underwater environment is complex, with dim lighting, and images are often blurry, making it difficult to accurately extract biological features. Second, underwater organisms are often concentrated in certain areas, leading to overlap and occlusion, which affects the accuracy of target recognition and presents challenges for annotation tasks. Lastly,the precise annotation of targets in underwater images requires specialized knowledge and technology, increasing both the cost and the difficulty of data preparation [15]. To address the above issues, this paper proposes a new method, SUD-YOLO, which combines the YOLOv8 object detection framework with the Mean Teacher semi-supervised learning strategy [16]. The main contributions of this paper are as follows:  
1. This study proposes a novel underwater object detection method based on the Mean Teacher semi-supervised learning method, which combines a limited number of labeled samples with a large number of unlabeled samples. The teacher model is used to guide the generation of pseudo-labels, and a multi-scale pseudo-label enhancement module is specially developed to address the issue of low-quality pseudo-labels. 2. This study proposes a module that integrates the receptive-field attention mechanism with local spatial features, significantly improving the feature extraction capability in underwater images. 3. This study proposes a lightweight detection head based on task alignment, which introduces shared convolution to reduce parameters while improving the feature delicacy in small target detection layers, allowing the model to observe more detailed features.