Deep learning-based detection from the perspective of small or tiny objects: A survey

Abstract

Detecting small or tiny objects is always a difficult and challenging issue in computer vision. In this paper, we provide a latest and comprehensive survey of deep learning-based detection approaches from the perspective of small or tiny objects. Our survey is featured by thorough and exhaustive analysis of small or tiny object detection. We comprehensively introduce 30 existing datasets about small or tiny objects, and summarize different definitions of small or tiny objects based on different application scenarios, such as pedestrian detection, traffic signs detection, face detection, remote sensing target detection and object detection in common life. Then small or tiny object detection techniques are overviewed systematically from seven aspects, including super-resolution techniques, context-based information, multi-scale representation learning, anchor mechanism, training strategy, data augmentation, and schemes based on loss function. Finally, the detection performance of small or tiny objects on 12 popular datasets is analyzed in depth. Based on performance analysis, we also discuss the promising research directions in the future. We hope this survey could provide researchers guidance to catalyze understanding of small or tiny object detection and further facilitate research on small or tiny object detection systems. • Summarize the 30 datasets about small or tiny objects. • Provide definitions of small/tiny objects based on different application scenarios. • Systematically review small/tiny object detection techniques from seven aspects. • Present the evaluation results of different methods for small/tiny object detection. • Discuss future research directions of small/tiny object detection.