Cross-Layer Attention Network for Small Object Detection in Remote Sensing Imagery
Yangyang Li, Qin Huang, Xuan Pei, Yanqiao Chen, Licheng Jiao, Ronghua Shang
Abstract
In recent years, despite the tremendous progresses of object detection, small object detection has always been a challenge in the field of remote sensing. The main reason is that small objects cover few features that are easily lost during down-sampling. In this article, we propose a cross-layer attention network aiming to obtain stronger features of small objects for better detection. Specifically, we designed an up-sampling and down-sampling feature pyramid to obtain richer context information by bidirectionally fusing deep and shallow features, as well as skipping connections. Moreover, a cross-layer attention module is designed to obtain the nonlocal association of small objects in each layer, and further strengthen its representation ability through cross-layer integration and balance. Extensive experiments on the publicly available datasets (DIOR dataset and NWPUVHR-10 dataset) and the self-assembled datasets (SDOTA dataset and SDD dataset) show the excellent performance of our method compared with other detectors. Moreover, our method achieved 74.3% mAP on the public DIOR dataset without any tricks.