Construction of a feature enhancement network for small object detection

Abstract

Limited by the size, location, number of samples and other factors of the small object itself, the small object is usually insufficient, which degrades the performance of the small object detection algorithms. To address this issue, we construct a novel Feature Enhancement Network (FENet) to improve the performance of small object detection. Firstly, an improved data augmentation method based on collision detection and spatial context extension (CDCI) is proposed to effectively expand the possibility of small object detection. Then, based on the idea of Granular Computing, a multi-granular deformable convolution network is constructed to acquire the offset feature representation at the different granularity levels. Finally, we design a high-resolution block (HR block) and build High-Resolution Block-based Feature Pyramid by parallel embedding HR block in FPN (HR-FPN) to make full use different granularity and resolution features. By above strategies, FENet can acquire sufficient feature information of small objects. In this paper, we firstly applied the multi-granularity deformable convolution to feature extraction of small objects. Meanwhile, a new feature fusion module is constructed by optimizing feature pyramid to maintain the detailed features and enrich the semantic information of small objects. Experiments show that FENet achieves excellent performance compared with performance of other methods when applied to the publicly available COCO dataset, VisDrone dataset and TinyPerson dataset. The code is available at https://github.com/cowarder/FENet.