Accurate small object detection via density map aided saliency estimation

Small object detection (SOD) in crowded scenes is a challenging task since objects are densely distributed and partially overlapped. In this paper, we propose a novel SOD method by fully exploring the information provided by the image and its estimated density map. Our proposed SOD method consists of two main stages. Initial object locations are firstly computed based on object spatial distribution information obtained from the estimated density maps. Inspired by the human visual attention mechanism, a saliency map which offers object boundaries is then employed to accurately estimate the bounding boxes with the support of the estimated initial object locations. Experimental results on three public small object datasets and a self-built snipe dataset demonstrate the effectiveness of our proposed SOD method, especially under small training set condition. It is encouraged to see that our SOD method only requires the dotted annotation training datasets and is able to estimate the bounding boxes fitting the shape of the objects accurately.