计算机科学
人工智能
模式识别(心理学)
特征(语言学)
棱锥(几何)
编码器
光学(聚焦)
联营
骨干网
特征提取
卷积神经网络
目标检测
解码方法
计算机视觉
数学
哲学
物理
光学
操作系统
电信
语言学
计算机网络
几何学
作者
Xiaozhong Tong,Shaojing Su,Peng Wu,Runze Guo,Junyu Wei,Zhen Zuo,Bei Sun
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:61: 1-16
被引量:9
标识
DOI:10.1109/tgrs.2023.3279253
摘要
The detection of small infrared targets with a low signal-to-noise ratios and contrasts in noisy and cluttered backgrounds is challenging and therefore a domain of active research. Traditional methods result in a large number of false alarms and missed detections. In the case of convolutional neural network-based methods, it may not be possible to identify deep small targets, or the details of the target’s edge contours may not be appropriately considered. Therefore, this paper proposes MSAFFNet to perform infrared small target detection based on an encoder-decoder framework. In the encoder stage, small target features are extracted using a resnet-20 backbone network, and the global contextual features of small targets are extracted using an atrous spatial pyramid pooling module. In the decoding stage, a dual-attention module is used to selectively enhance the spatial details of the target at the shallow level and representative features of the semantic information at the deep level. Multi-scale feature maps are then concatenated to achieve superior feature fusion. Additionally, multi-scale labels are constructed to focus on the details of the target contour and internal features based on edge information and an internal feature aggregation module. Experiments conducted on the NUAA-SIRST, NUDT-SIRST and XDU-SIRST datasets revealed that the proposed approach outperforms the representative methods and achieves an improved detection performance.
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