Dense sampling and detail enhancement network: Improved small object detection based on dense sampling and detail enhancement

Abstract

Abstract Small objects only occupy a few pixels in an image, which results in low performance of small object detection for existing object detection algorithms. Therefore, the authors propose a dense sampling and detail enhancement network (DSDE‐Net) to address this issue. The network contains a dense sampling module used to increase the resolution of feature maps and expand the receptive field, which includes an atrous spatial pyramid pooling network and a coordinate attention mechanism to systematically process feature maps. Simultaneously, the authors introduce a detail enhancement branch that contains edge and detailed information to generate detailed enhancement feature maps through Gaussian filtering to compensate for the loss of small object information that occurs in the feature extraction process. The experimental results demonstrate that the proposed network outperformed related methods. Compared with the state‐of‐the‐art algorithm DetectoRS, it effectively achieves approximately 4.6% improvement on the minicoco2021 dataset and 4.2% improvement on the remotely sensed dataset VisDrone.

Keywords

• computer vision
• dense sampling
• detail enhancement
• object detection