水下垃圾检测是水下机器人处理水下垃圾的关键技术。然而，水下环境的复杂多变和光照条件的不佳，以及传统卷积神经网络（convolutional neural networks, CNN）模型中步长卷积导致的细节信息丢失和低分辨率图像表现不佳等问题，限制了现有模型的准确度和速度。为了解决这些问题，提出了一种新型的水下垃圾检测算法SPDC-YOLOv8（small proposal detection convolution-YOLOv8）。该算法在YOLOv8的主干网络中采用了基于自适应空间分解的CNN模块SPD-Conv（space-to-depth convolution），替换了步长卷积，从而提高了模型对低分辨率图像和小物体检测的精确性。同时，在模型的上采样过程中使用了CARAFE（content-aware reassembly of features）算子，增强了水下垃圾的语义信息和特征表达能力，进而提高了目标检测的鲁棒性。实验结果表明，提出的方法在trash_ICRA19数据集和TrashCan数据集上分别获得了98.6%和91.2%的准确率，相比原始YOLOv8模型分别提高了0.3%和0.8%，计算时间均为2.6 ms。本文中所提出的改进后的YOLOv8算法更适应水下复杂环境的检测任务。

Underwater trash detection is considered a key technology for underwater robots to handle underwater trash. However, the complexity and variability of the underwater environment, poor lighting conditions, the loss of detailed information, and the poor performance of low-resolution images due to step-length convolution in traditional CNN(convolutional neural networks) models limit the accuracy and speed of existing models. In order to solve these problems, a novel underwater rubbish detection algorithm named SPDC-YOLOv8 (small proposal detection convolution-YOLOv8) was proposed. The algorithm employed an adaptive spatial decomposition-based CNN module SPD-Conv(space-to-depth convolution) in the backbone network of YOLOv8, replacing the step-length convolution, thus improving the accuracy of the model for low-resolution images and small object detection. Meanwhile, the CARAFE(contentaware reassembly of features) was employed in the up-sampling process of the model, which enhanced the semantic information and feature representation of underwater rubbish, thus improving the robustness of object detection. The experimental results demonstrate that the method proposed achieves 98.6% and 91.2% accuracy on the trash_ICRA19 dataset and TrashCan dataset, respectively, and improves by 0.3% and 0.8% compared to the original YOLOv8 model, with a the computation time is 2.6 ms in both cases. The improved YOLOv8 algorithm proposed is found to be more adaptable to the complex underwater environment.