Background and foreground attention maps for small object detection

Hao Zhang; Danpu Zhang; Jia Liu; Jianfeng Yang; Pengfei Shi

doi:10.1117/12.3011350

8 November 2023 Background and foreground attention maps for small object detection

Hao Zhang, Danpu Zhang, Jia Liu, Jianfeng Yang, Pengfei Shi

Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 129230Y (2023) https://doi.org/10.1117/12.3011350
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China

Abstract

We observe representative information of large, medium objects and the vast background overwhelm that of small objects, which leads to poor performance of small object detection. To this end, a new module has been proposed in this paper, named Background and Foreground Attention Maps(BFAM) module, composing of three sub-modules: segmentation, background and foreground attention sub-modules. The segmentation maps positions which have the top strong semantic information in the deep layer of the backbone to the bottom layer maintaining more small object details and mask them to obtain background map and foreground map. Apply two tailored attention sub-modules on them respectively and then fuse them with different weights to detect final results. Experiments demonstrate BFAM achieves promising gains in small object detection on PASCAL VOC 2012 and Seaship datasets.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hao Zhang, Danpu Zhang, Jia Liu, Jianfeng Yang, and Pengfei Shi "Background and foreground attention maps for small object detection", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 129230Y (8 November 2023); https://doi.org/10.1117/12.3011350

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