Multimodal Few-Shot Target Detection Based on Uncertainty Analysis in Time-Series Images

The ability to interpret multimodal data, and map the targets and anomalies within, is important for an automatic recognition system. Due to the expensive and time-consuming nature of multimodal time-series data annotation in the training stage, multimodal time-series image understanding, from drone and quadruped mobile robot platforms, is a challenging task for remote sensing and photogrammetry. In this regard, robust methods must be computationally low-cost, due to the limited data on aerial and ground-based platforms, yet accurate enough to meet certainty measures. In this study, a few-shot learning architecture, based on a squeeze-and-attention structure, is proposed for multimodal target detection, using time-series images from the drone and quadruped robot platforms with a small training dataset. To build robust algorithms in target detection, a squeeze-and-attention structure has been developed from multimodal time-series images from limited training data as an optimized method. The proposed architecture was validated on three datasets with multiple modalities (e.g., red-green-blue, color-infrared, and thermal), achieving competitive results.