论文标题
tinyCD：一个（不是）深度学习模型用于变更检测
TINYCD: A (Not So) Deep Learning Model For Change Detection
论文作者
论文摘要
在本文中，我们提出了一种称为TinyCD的轻巧有效的更改检测模型。由于工业需求，该模型的设计速度比当前的最新变更检测模型更快，更小。尽管比比较的变更检测模型小的13倍至140倍，并且至少揭示了计算复杂性的三分之一，但我们的模型在F1分数和Levir-CD数据集上的最新模型的表现均优于当前的最新模型$ 1 \％$，而在WHU-CD数据集中的$ 8 \％$ $ 8 \％。为了达到这些结果，TinyCD使用暹罗U-NET体系结构，以全球时间和本地空间方式利用低级功能。此外，它采用了一种新策略来混合时空域中的特征，以合并从暹罗主链获得的嵌入，并与MLP块相结合，形成了一种新型的空间语义注意机制，混合物和注意力面膜块（MAMB）。源代码，模型和结果可在此处找到：https：//github.com/andreacodegoni/tiny_model_4_cd
In this paper, we present a lightweight and effective change detection model, called TinyCD. This model has been designed to be faster and smaller than current state-of-the-art change detection models due to industrial needs. Despite being from 13 to 140 times smaller than the compared change detection models, and exposing at least a third of the computational complexity, our model outperforms the current state-of-the-art models by at least $1\%$ on both F1 score and IoU on the LEVIR-CD dataset, and more than $8\%$ on the WHU-CD dataset. To reach these results, TinyCD uses a Siamese U-Net architecture exploiting low-level features in a globally temporal and locally spatial way. In addition, it adopts a new strategy to mix features in the space-time domain both to merge the embeddings obtained from the Siamese backbones, and, coupled with an MLP block, it forms a novel space-semantic attention mechanism, the Mix and Attention Mask Block (MAMB). Source code, models and results are available here: https://github.com/AndreaCodegoni/Tiny_model_4_CD
