Abstract: Sea treasure target detection is a key technology for the intelligent development of sea treasure resources. This paper proposes an improved algorithm YOLOv9-PAEG based on YOLOv9-S to address the problem of low accuracy in detecting sea treasures in complex underwater environments, difficult feature extraction, diverse target sizes, and a large number of small targets. Firstly, the SPPELAN module was improved by introducing the PfAAM attention mechanism and distributed shift convolution DSConv2D, and the PFAD_SPPELAN module was designed to enhance the detection accuracy and speed of the model. Secondly, by introducing a variable kernel convolution AKConv in the backbone network layer of the model, the model can more flexibly adapt to features of different sizes and shapes, thereby improving its feature extraction ability for multi-scale targets, especially small targets. Then, the ECA attention mechanism was integrated into the neck layer of the model, enhancing its ability to represent important features and improving detection accuracy. Finally, by using the GIoU loss function, the convergence of the model was accelerated and the positioning accuracy was optimized. Experiments have shown that the YOLOv9-PAEG model performs well on datasets DUO and UDD mAP@0.5 They reached 89.7% and 77.6% respectively, and FPS reached 71 and 69, respectively. Compared with the original model and other mainstream object detection models, they have improved detection accuracy and speed. This fully proves the effectiveness and progressiveness of the YOLOv9-PAEG model, which can provide a better detection effect for marine treasures.

Key words: sea treasure target detection, YOLOv9-S, akconv, attention mechanisms

摘要： 海珍品目标检测是海珍品资源智能化开发的关键性技术。针对水下环境复杂、特征提取困难、目标尺寸各异以及小目标较多导致海珍品目标检测精度低的问题，该研究提出了一种基于YOLOv9-S的改进算法YOLOv9-PAEG。首先，通过引入PfAAM注意力机制和分布移位卷积DSConv2D对SPPELAN模块进行改进，设计出PFAD_SPPELAN模块，提高了模型的检测精度和速度。其次，在模型的骨干网络层引入可改变核卷积AKConv，模型能够更灵活地适应不同大小和形状的特征，从而提高对多尺寸目标、尤其是对小目标的特征提取能力。然后，在模型的颈部层中融入ECA注意力机制，模型对重要特征的表示能力得到了增强，进而提升了检测精度。最后，通过采用GIoU损失函数，模型的收敛得到了加速，定位精度也得到了优化。试验表明，在数据集DUO和UDD上，YOLOv9-PAEG模型的mAP@0.5分别达到了89.7%、77.6%，FPS分别达到了71、69，相比于原模型和其他主流的目标检测模型在检测精度和速度上均有所提升。这充分证明了YOLOv9-PAEG模型的有效性和先进性，能够为海珍品提供更好的检测效果。

关键词: 海珍品目标检测, YOLOv9-S, AKConv, 注意力机制