【摘要】 水下目标检测已成为计算机视觉的热门研究领域之一，现有的水下场景数据集中的水下目标存在尺度分布跨度大且分布密集的问题。针对现有光学成像水下目标检测技术在挖掘、表征水下目标多尺度特征方面的局限性，提出了基于尺度序列特征金字塔和金字塔分割注意力机制的YOLOv7水下目标检测算法。首先，在YOLOv7算法基础上引入了一种新的尺度序列特征金字塔，以增强模型的多尺度特征提取能力；然后，引入一种高效的金字塔分割注意力机制，以提升模型提取细粒度多尺度空间信息的能力；最后，在2个公共数据集上进行了试验。试验结果表明，该算法在多种水体的不同目标上都能表现出较好的性能。更多还原

【Abstract】 Underwater object detection has become one of the hot research areas in computer vision.However, existing underwater scene datasets have issues such as a wide range of scale distributions and dense distribution of underwater targets. Addressing the limitations of existing optical imaging underwater object detection technologies in mining and characterizing multi-scale features of underwater objects, a YOLOv7 underwater object detection algorithm based on a scale-sequential feature pyramid and a pyramid partition attention mechanism. Firstly, a new scale-sequential feature pyramid is introduced on the basis of YOLOv7 to enhance the model’s multi-scale feature extraction capabilities.Then, an efficient pyramid partition attention mechanism is introduced to improve the model’s ability of extracting more fine-grained multi-scale spatial information. Finally, experiments conducted on two public datasets are carried out. Experiment result shows that the algorithm performs well on various underwater targets across different water bodies.更多还原