In real-world traffic, there are various uncertainties and complexities in road and weather conditions. To solve the problem that the feature information of pole-like obstacles in complex environments is easily lost, resulting in low detection accuracy and low real-time performance, a multi-scale hybrid attention mechanism detection algorithm is proposed in this paper. First, the optimal transport function Monge-Kantorovich (MK) is incorporated not only to solve the problem of overlapping multiple prediction frames with optimal matching but also the MK function can be regularized to prevent model over-fitting; then, the features at different scales are up-sampled separately according to the optimized efficient multi-scale feature pyramid. Finally, the extraction of multi-scale feature space channel information is enhanced in complex environments based on the hybrid attention mechanism, which suppresses the irrelevant complex environment background information and focuses the feature information of pole-like obstacles. Meanwhile, this paper conducts real road test experiments in a variety of complex environments. The experimental results show that the detection precision, recall, and average precision of the method are 94.7%, 93.1%, and 97.4%, respectively, and the detection frame rate is 400 f/s. This research method can detect pole-like obstacles in a complex road environment in real time and accurately, which further promotes innovation and progress in the field of automatic driving.

提出了一种基于多尺度混合注意力机制的检测算法，通过引入最优传输函数 Monge-Kantorovich 解决了复杂环境中多帧预测的重叠问题，优化的多尺度特征金字塔实现了对不同尺度特征的上采样，通过混合注意力机制改进了多尺度特征空间通道信息的提取，实现了对复杂道路环境中类似杆状障碍物的快速准确检测。实验结果表明，该方法的检测精度、召回率和平均精度分别为94.7%、93.1%和97.4%，检测帧率为400 f/s，能够有效地推动自动驾驶领域的创新和进步。

MFMAN-YOLO：复杂环境中检测类似于杆状障碍物的方法