Generate-Paste-Blend-Detect: Synthetic dataset for object detection in the agriculture domain
Nikolaos Giakoumoglou, Eleftheria Maria Pechlivani, Dimitrios Tzovaras
Abstract
Object detection is a challenging task, hindered by the scarcity of large annotated datasets. In agriculture, the lack of annotated insect datasets often results in domain-specific models that lack generalization. Data collection and annotation can be expensive and time-consuming. This paper proposes a simple approach to generate synthetic datasets for object detection that requires only a small dataset of target objects and a larger background dataset that fits the desired environment. The approach named Generate-Paste-Blend-Detect uses Denoising Diffusion Probabilistic Models (DDPM) to artificially “generate” objects, “paste” them on a background image, “blend” them with the environment to avoid pixel artifacts which result in poor performance for trained models, and finally use an object detection model to “detect” the artificially added object instances. The proposed methodology is demonstrated in the agricultural domain to detect whiteflies achieving a mean average precision (mAP50) of 0.66 with the state-of-the-art YOLOv8 object detection model. This approach enables domain-specific detection with minimal labor and cost.