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Small object detection is a fundamental and challenging issue in computer vision. We believe that there are two factors that affect the performance of small object detection: small object dataset and small object itself. In terms of datasets, we introduce a dataset named SeaDefine, which opens up a new direction for small object detection in maritime environment. For the small object itself, we utilize deep feature learning and feature fusion network (DFLFFN) to help detect objects. Concretely, the designed deep feature learning module (DFLM) at the single-layer level can describe objects for a variety of scenarios through activating multi-scale receptive fields over a wider scope. Meanwhile, to intensify classification capacity of small objects, the shallow features with rich details will be integrated with the deep features generated from the DFLM by introducing feature fusion block (FFB). In addition, we analyze the multi-scale strategy from a mathematical perspective to a certain extent. A large number of results in the experiments show that proposed DFLFFN achieves the leading detection performance on the MS-COCO and SeaDefine datasets. In particular, our DFLFFN surpasses the baseline by 4.1 points on APrs score for SeaDefine dataset, and 7.8 points on AP S score for MS-COCO dataset. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]