DODA: Adapting Object Detectors to Dynamic Agricultural Environments in Real-Time with Diffusion
This addresses the practical challenge for agricultural growers who need real-time adaptation to constantly changing environments, though it appears incremental as it builds on existing diffusion and domain adaptation methods.
The paper tackles the problem of object detectors failing due to domain shifts in agricultural environments by proposing DODA, a diffusion-based framework that adapts detectors to new domains in just 2 minutes without retraining, achieving significant improvements on the Global Wheat Head Detection dataset.
Object detection has wide applications in agriculture, but domain shifts of diverse environments limit the broader use of the trained models. Existing domain adaptation methods usually require retraining the model for new domains, which is impractical for agricultural applications due to constantly changing environments. In this paper, we propose DODA ($D$iffusion for $O$bject-detection $D$omain Adaptation in $A$griculture), a diffusion-based framework that can adapt the detector to a new domain in just 2 minutes. DODA incorporates external domain embeddings and an improved layout-to-image approach, allowing it to generate high-quality detection data for new domains without additional training. We demonstrate DODA's effectiveness on the Global Wheat Head Detection dataset, where fine-tuning detectors on DODA-generated data yields significant improvements across multiple domains. DODA provides a simple yet powerful solution for agricultural domain adaptation, reducing the barriers for growers to use detection in personalised environments. The code is available at https://github.com/UTokyo-FieldPhenomics-Lab/DODA.