Small open vocabulary object detection from drone images using OWL-VIT combined with SAHI

Abstract

The demand for precise and efficient object detection in aerial imagery has surged, driven by applications in agriculture, surveillance, disaster management, and environmental monitoring. However, detecting small objects in drone-captured images remains challenging due to factors like low resolution, occlusion, and varying scales. This research explores a novel approach to small, open vocabulary object detection by combining the OWL-ViT (OpenWorld Vision Transformer) model with the SAHI (Slicing Aided Hyper Inference) technique. OWL-ViT, known for its ability to handle open vocabulary object detection, is leveraged for its robust feature extraction and generalization capabilities across diverse object categories. SAHI is integrated to address the small object detection challenge by slicing high resolution drone images into smaller patches, enabling more focused and detailed inference. In a comprehensive evaluation, our combined method achieves significant improvements in mAP@50 for small-scale object detection, with an average increase of +6.8% on the VisDrone dataset.