An efficient single shot detector with weight-based feature fusion for small object detection

Ming Li; Dechang Pi; Shuo Qin

doi:10.1038/s41598-023-36972-x

An efficient single shot detector with weight-based feature fusion for small object detection

Sci Rep. 2023 Jun 19;13(1):9883. doi: 10.1038/s41598-023-36972-x.

Authors

Ming Li^{1

2}, Dechang Pi³, Shuo Qin¹

Affiliations

¹ School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211100, China.
² Beijing Institute of Environmental Features, Beijing, 100854, China.
³ School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211100, China. pinuaa@nuaa.edu.cn.

Abstract

Object detection has been widely applied in various fields with the rapid development of deep learning in recent years. However, detecting small objects is still a challenging task because of the limited information in features and the complex background. To further enhance the detection accuracy of small objects, this paper proposes an efficient single-shot detector with weight-based feature fusion (WFFA-SSD). First, a weight-based feature fusion block is designed to adaptively fuse information from several multi-scale feature maps. The feature fusion block can exploit contextual information for feature maps with large resolutions. Then, a context attention block is applied to reinforce the local region in the feature maps. Moreover, a pyramids aggregation block is applied to combine the two feature pyramids to classify and locate target objects. The experimental results demonstrate that the proposed WFFA-SSD achieves higher mean Average Precision (mAP) under the premise of ensuring real-time performance. WFFA-SSD increases the mAP of the car by 4.12% on the test set of the CARPK.