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In real-world traffic, there are various uncertainties and complexities inroad and weather conditions. To solve the problem that the feature informationof pole-like obstacles in complex environments is easily lost, resulting in lowdetection accuracy and low real-time performance, a multi-scale hybridattention mechanism detection algorithm is proposed in this paper. First, theoptimal transport function Monge-Kantorovich (MK) is incorporated not only tosolve the problem of overlapping multiple prediction frames with optimalmatching but also the MK function can be regularized to prevent modelover-fitting; then, the features at different scales are up-sampled separatelyaccording to the optimized efficient multi-scale feature pyramid. Finally, theextraction of multi-scale feature space channel information is enhanced incomplex environments based on the hybrid attention mechanism, which suppressesthe irrelevant complex environment background information and focuses thefeature information of pole-like obstacles. Meanwhile, this paper conducts realroad test experiments in a variety of complex environments. The experimentalresults show that the detection precision, recall, and average precision of themethod are 94.7%, 93.1%, and 97.4%, respectively, and the detection frame rateis 400 f/s. This research method can detect pole-like obstacles in a complexroad environment in real time and accurately, which further promotes innovationand progress in the field of automatic driving.

MFMAN-YOLO: A Method for Detecting Pole-like Obstacles in Complex Environment