  Frequent mine disasters cause a large number of casualties and property
losses. Autonomous driving is a fundamental measure for solving this problem,
and track detection is one of the key technologies for computer vision to
achieve downhole automatic driving. The track detection result based on the
traditional convolutional neural network (CNN) algorithm lacks the detailed and
unique description of the object and relies too much on visual postprocessing
technology. Therefore, this paper proposes a track detection algorithm based on
a multiscale conditional generative adversarial network (CGAN). The generator
is decomposed into global and local parts using a multigranularity structure in
the generator network. A multiscale shared convolution structure is adopted in
the discriminator network to further supervise training the generator. Finally,
the Monte Carlo search technique is introduced to search the intermediate state
of the generator, and the result is sent to the discriminator for comparison.
Compared with the existing work, our model achieved 82.43\% pixel accuracy and
an average intersection-over-union (IOU) of 0.6218, and the detection of the
track reached 95.01\% accuracy in the downhole roadway scene test set.
