Small object detection using deep feature learning and feature fusion network

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 APS score for MS-COCO dataset.