  The DCELANM-Net structure, which this article offers, is a model that
ingeniously combines a Dual Channel Efficient Layer Aggregation Network
(DCELAN) and a Micro Masked Autoencoder (Micro-MAE). On the one hand, for the
DCELAN, the features are more effectively fitted by deepening the network
structure; the deeper network can successfully learn and fuse the features,
which can more accurately locate the local feature information; and the
utilization of each layer of channels is more effectively improved by widening
the network structure and residual connections. We adopted Micro-MAE as the
learner of the model. In addition to being straightforward in its methodology,
it also offers a self-supervised learning method, which has the benefit of
being incredibly scaleable for the model.
