TINYCD: A (Not So) Deep Learning Model For Change Detection

Andrea Codegoni, Gabriele Lombardi, Alessandro Ferrari
7/26/2022
cs.CVcs.LGeess.IV

Official code: AndreaCodegoni/Tiny_model_4_CD (133 stars, last commit 2024-05-09).

Abstract

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

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AndreaCodegoni/Tiny_model_4_CDOfficial100%

Official implementation of TinyCD: A (Not So) Deep Learning Model For Change Detection

13324PythonJun 15, 20222 years ago

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Cite this paper

@article{codegoni2022tinycd,
  title  = {TINYCD: A (Not So) Deep Learning Model For Change Detection},
  author = {Andrea Codegoni and Gabriele Lombardi and Alessandro Ferrari},
  year   = {2022},
  eprint = {2207.13159},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2207.13159v2}
}

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