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

Code

• github.com/andreacodegoni/tiny_model_4_cd
  OfficialIn paperpytorch★ 133
• github.com/likyoo/open-cd
  pytorch★ 831

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

Tasks

Benchmark Results

Reproductions