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.

翻译：在真实交通场景中，道路与气象条件存在诸多不确定性与复杂性。针对复杂环境下杆状障碍物特征信息易丢失、检测精度低及实时性差的问题，本文提出一种多尺度混合注意力机制检测算法。首先，引入最优传输函数Monge-Kantorovich（MK），不仅解决多预测框重叠时的最优匹配问题，还通过正则化MK函数防止模型过拟合；其次，基于优化后的高效多尺度特征金字塔，对不同尺度的特征分别进行上采样；最后，基于混合注意力机制增强复杂环境下多尺度特征空间通道信息的提取能力，抑制无关的复杂环境背景信息，聚焦杆状障碍物的特征信息。同时，本文在多种复杂环境下开展实际道路测试实验。实验结果表明，该方法的检测精度、召回率和平均精度分别达94.7%、93.1%和97.4%，检测帧率为400 f/s。本研究方法可实时准确检测复杂道路环境中的杆状障碍物，进一步推动自动驾驶领域的创新与发展。