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.
Highlights
T HERE are many different research branches in remote sensing field, such as object detection, change detection, object tracking, and anomaly detection
We proposed an improved detector based on faster R-convolutional neural network (CNN), for detecting small objects in the field of remote sensing, named CANet
Compared with the existing detectors, CANet can effectively enrich the features of small objects, which can address the problem that small objects themselves carry little effective information and have weaker representation capabilities
Summary
T HERE are many different research branches in remote sensing field, such as object detection, change detection, object tracking, and anomaly detection. With the improvement of computer hardware, a lot of excellent works [1]– [5] has emerged in remote sensing field, which further promotes the research in remote sensing field. Just as humans discover objects, the object detectors need to tell us where the objects are, and what the objects are. This is a very simple task for humans but an extremely difficult task for neural networks
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More From: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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