复杂背景下低信噪比红外弱小目标检测方法

doi:10.3778/j.issn.1002-8331.2312-0359

摘要/Abstract

摘要： 复杂背景下红外弱小目标与背景难以区分，信噪比低，检测十分困难。为了解决这一问题，提出了一种新的卷积神经网络。通过特征增强模块，将目标的灰度分布特性编码至网络，实现红外弱小目标的特征增强；提出平衡双向特征融合模块，改进深层和浅层特征融合方式，实现浅层细节信息和高层语义信息的平衡，进一步提升网络的特征提取能力。在公开数据集上定性和定量地对比了方法的检测性能，并进一步比较了在不同信噪比条件下目标检测能力。实验结果表明：提出的方法相比于三种非监督方法和两种监督方法在检测率和虚警率性能方面均有显著提升，在虚警率为1×10?5条件下检测率达到76.20%。

关键词: 红外图像, 目标检测, 卷积神经网络, 低信噪比

Abstract: It is difficult to distinguish small infrared targets from the background in complex backgrounds, and the signal-to-noise ratio is low, making detection very difficult. In order to solve this problem, a new convolutional neural network is proposed. Through the feature enhancement module, the gray distribution characteristics of the target are encoded into the network to realize the feature enhancement of infrared dim and small targets. A balanced bidirectional fusion module is proposed to improve the deep and shallow feature fusion methods, realize the balance between shallow detail information and high-level semantic information, and further improve the feature extraction ability of the network. The results of the qualitative and quantitative detection performance of the method are compared on the public dataset, and the effects of the target detection ability under different signal-to-noise ratio conditions are further compared. Experimental results show that compared with the three unsupervised methods and the two supervised methods, the proposed method has a significant improvement in the detection rate and false alarm rate performance, and the detection rate reaches 76.20% under the condition of 1×10?5 false alarm rate.

Key words: infrared image, target detection, convolutional neural networks, low signal-to-noise ratio

孟维超, 卞春江, 聂宏宾. 复杂背景下低信噪比红外弱小目标检测方法[J]. 计算机工程与应用, 2025, 61(8): 183-193.

MENG Weichao, BIAN Chunjiang, NIE Hongbin. Method for Detecting Dim Small Infrared Targets with Low Signal-to-Noise Ratio in Complex Background[J]. Computer Engineering and Applications, 2025, 61(8): 183-193.

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