Construction of a feature enhancement network for small object detection

•To more effectively expand the possibility of small objects appearing, we improve current copy-paste based data augmentation method (CDCI) by introducing collision detection and spatial context position extension to avoid object collision and incorrect context information caused by random copy-paste.•To solve the problem that the small objects are vulnerable to scale variation, we construct a multi-granular deformable convolution network to learn and capture the changes in the shape and scale of the object, and offset feature representations in different granularity are acquire by granulating and fusing the offset features.•A high-resolution block (HR block) is designed to bring more semantic information while maintaining high-resolution features, and high-resolution block-based Feature Pyramid is built by parallel embedding HR block in FPN to further enhancing the feature representation.•A large number of experiments are reported to demonstrate the effectiveness of the proposed method. At the same time, we set up ablation experiments to analyze the rationality of proposed different strategies.