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.

翻译：本文提出的DCELANM-Net结构是一种巧妙结合双通道高效层聚合网络（DCELAN）与微型掩码自编码器（Micro-MAE）的模型。一方面，针对DCELAN，通过深化网络结构可更高效地拟合特征；深层网络能够成功学习并融合特征，从而更精准地定位局部特征信息；通过拓宽网络结构与引入残差连接，各层通道的利用率得到有效提升。我们采用Micro-MAE作为模型的学习器。该方法不仅实现方式简洁直接，还提供了一种自监督学习方法，其优势在于可为模型带来极佳的可扩展性。