TINYCD: A (not so) deep learning model for change detection
A Codegoni, G Lombardi, A Ferrari - Neural Computing and Applications, 2023 - Springer
A Codegoni, G Lombardi, A Ferrari
Neural Computing and Applications, 2023•SpringerIn 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 …
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 …
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 on both F1-score and IoU on the LEVIR-CD dataset, and more than 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).
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