在复杂的体育场景中，对运动目标进行识别与检测时存在检测效率和精度低下、环境噪声多等问题，故提出了YOLOv9多注意力融合的机制。首先，通过在输出端引入多尺度扩展注意力机制（multi-scale dilated attention, MSDA）在不同尺度上理解图像，从而增强获取特征的能力和细节捕捉能力。其次，在输出端引入可变性大核注意力机制（deformable large kernel convolution, DLKA）使模型能适应不同数据的模式，提高适应复杂环境的图像识别的能力。实验证明，改进的多注意力机制融合算法在运动员检测中，mAP50和mAP75相对于基本模型分别提高1.4%和1.7%。验证了所提算法具有显著的运动目标识别能力，对智慧建设和智慧体育中运动目标的检测提供了重要的基础。

In complex sports scenarios, there are some problems in the recognition and detection of sports targets, such as low detection efficiency and accuracy, and many environmental noises. Therefore, a mechanism of multi attention fusion of YOLOv9 was proposed. Firstly, the multi-scale dilated attention (MSDA) mechanism was introduced at the output end to understand the image at different scales, so as to enhance the ability to obtain features and capture details. Secondly, the deformable large kernel convolution(DLKA) mechanism was introduced at the output end to make the model adapt to the patterns of different data and improve the ability of image recognition to adapt to complex environments. Experiments show that mAP50 and mAP75 increase 1.4% and 1.7% respectively compared with the basic model in athlete detection. It is verified that the algorithm has significant motion target recognition ability, which provides an important foundation for the detection of motion targets in smart construction and smart sports.