MFMAN-YOLO: A Method for Detecting Pole-like Obstacles in Complex
  Environment
Lei Cai Hao Wang Congling Zhou Yongqiang Wang Boyu Liu
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.