Dim Moving Small Target Detection by Local and Global Variance Filtering on Temporal Profiles in Infrared Sequences

In this paper, the temporal different characteristics between the target and background pixels are used to detect dim moving targets in the slow-evolving complex background. A local and global variance filter on temporal profiles is presented that addresses the temporal characteristics of the target and background pixels to eliminate the large variation of background temporal profiles. Firstly, the temporal behaviors of different types of image pixels of practical infrared scenes are analyzed.Then, the new local and global variance filter is proposed. The baseline of the fluctuation level of background temporal profiles is obtained by using the local and global variance filter. The height of the target pulse signal is extracted by subtracting the baseline from the original temporal profiles. Finally, a new target detection criterion is designed. The proposed method is applied to detect dim and small targets in practical infrared sequence images. The experimental results show that the proposed algorithm has good detection performance for dim moving small targets in the complex background.

A Novel Filtering-Based Detection Method for Small Targets in Infrared Images

Infrared small target detection technology plays a pivotal role in critical military applications,including early warning systems and precision guidance for missiles and other defense mechanisms.Nevertheless,existing traditional methods face several significant challenges,including low background suppression ability,low detection rates,and high false alarm rates when identifying infrared small targets in complex environments.This paper proposes a novel infrared small target detection method based on a transformed Gaussian filter kernel and clustering approach.The method provides improved background suppression and detection accuracy compared to traditional techniques while maintaining simplicity and lower computational costs.In the first step,the infrared image is filtered by a new filter kernel and the results of filtering are normalized.In the second step,an adaptive thresholding method is utilized to determine the pixels in small targets.In the final step,a fuzzy C-mean clustering algorithm is employed to group pixels in the same target,thus yielding the detection results.The results obtained from various real infrared image datasets demonstrate the superiority of the proposed method over traditional approaches.Compared with the traditional method of state of the arts detection method,the detection accuracy of the four sequences is increased by 2.06%,0.95%,1.03%,and 1.01%,respectively,and the false alarm rate is reduced,thus providing a more effective and robust solution.

Improved Weighted Local Contrast Method for Infrared Small Target Detection

In order to address the problem of high false alarm rate and low probabilities of infrared small target detection in complex low-altitude background,an infrared small target detection method based on improved weighted local contrast is proposed in this paper.First,the ratio information between the target and local background is utilized as an enhancement factor.The local contrast is calculated by incorporating the heterogeneity between the target and local background.Then,a local product weighted method is designed based on the spatial dissimilarity between target and background to further enhance target while suppressing background.Finally,the location of target is obtained by adaptive threshold segmentation.As experimental results demonstrate,the method shows superior performance in several evaluation metrics compared with six existing algorithms on different datasets containing targets such as unmanned aerial vehicles(UAV).

Infrared Image Small Target Detection Based on Bi-orthogonal Wavelet and Morphology

An image multi-scale edge detection method based on anti-symmetrical bi-orthogonal wavelet is given in theory. Convolution operation property and function as a differential operator are analyzed,which anti-symmetrical bi-orthogonal wavelet transform have. An algorithm for wavelet reconstruction in which multi-scale edge can be detected is put forward. Based on it, a detection method for small target in infrared image with sea or sky background based on the anti-symmetrical bi-orthogonal wavelet and morphology is proposed. The small target detection is considered as a process in which structural background is removed, correlative background is suppressed, and noise is restrained. In this approach, the multi-scale edge is extracted by means of the anti-symmetrical bi-orthogonal wavelet decomposition. Then, module maximum chains formed by complicated background of clouds, sea wave and sea-sky-line are removed, and the image background becomes smoother. Finally, the morphology based edge detection method is used to get small target and restrain undulate background and noise. Experiment results show that the approach can suppress clutter background and detect the small target effectively.

Novel detection method for infrared small targets using weighted information entropy

This paper presents a method for detecting the small infrared target under complex background. An algorithm, named local mutation weighted information entropy （LMWIE）, is proposed to suppress background. Then, the grey value of targets is enhanced by calculating the local energy. Image segmentation based on the adaptive threshold is used to solve the problems that the grey value of noise is enhanced with the grey value improvement of targets. Experimental results show that compared with the adaptive Butterworth high-pass filter method, the proposed algorithm is more effective and faster for the infrared small target detection.

Infrared Small Target Detection Algorithm Based on ISTD-CenterNet

This paper proposes a real-time detection method to improve the Infrared small target detection CenterNet(ISTD-CenterNet)network for detecting small infrared targets in complex environments.The method eliminates the need for an anchor frame,addressing the issues of low accuracy and slow speed.HRNet is used as the framework for feature extraction,and an ECBAM attention module is added to each stage branch for intelligent identification of the positions of small targets and significant objects.A scale enhancement module is also added to obtain a high-level semantic representation and fine-resolution prediction map for the entire infrared image.Besides,an improved sensory field enhancement module is designed to leverage semantic information in low-resolution feature maps,and a convolutional attention mechanism module is used to increase network stability and convergence speed.Comparison experiments conducted on the infrared small target data set ESIRST.The experiments show that compared to the benchmark network CenterNet-HRNet,the proposed ISTD-CenterNet improves the recall by 22.85%and the detection accuracy by 13.36%.Compared to the state-of-the-art YOLOv5small,the ISTD-CenterNet recall is improved by 5.88%,the detection precision is improved by 2.33%,and the detection frame rate is 48.94 frames/sec,which realizes the accurate real-time detection of small infrared targets.

CAFUNeT:A small infrared target detection method in complex backgrounds

Small infrared target detection has widespread applications in various fields including military,aviation,and medicine.However,detecting small infrared targets in complex backgrounds remains challenging.To detect small infrared targets,we propose a variable-structure U-shaped network referred as CAFUNet.A central differential convolution-based encoder,ASPP,an Attention Fusion module,and a decoder module are the critical components of the CAFUNet.The encoder module based on central difference convolution effectively extracts shallow detail information from infrared images,complemented by rich contextual information obtained from the deep features in the decoder module.However,the direct fusion of the shallow detail features with semantic features may lead to feature mismatch.To address this,we incorporate an Attention Fusion(AF)module to enhance the network performance further.We performed ablation studies on each module to evaluate its effectiveness.The results show that our proposed algorithm outperforms the state-of-the-art methods on publicly available datasets.

Using deep learning to detect small targets in infrared oversampling images

According to the oversampling imaging characteristics, an infrared small target detection method based on deep learning is proposed. A 7-layer deep convolutional neural network(CNN) is designed to automatically extract small target features and suppress clutters in an end-to-end manner. The input of CNN is an original oversampling image while the output is a cluttersuppressed feature map. The CNN contains only convolution and non-linear operations, and the resolution of the output feature map is the same as that of the input image. The L1-norm loss function is used, and a mass of training data is generated to train the network effectively. Results show that compared with several baseline methods, the proposed method improves the signal clutter ratio gain and background suppression factor by 3–4 orders of magnitude, and has more powerful target detection performance.

Multi-Channel Based on Attention Network for Infrared Small Target Detection

Infrared detection technology has the advantages of all-weather detection and good concealment,which is widely used in long-distance target detection and tracking systems.However,the complex background,the strong noise,and the characteristics of small scale and weak intensity of targets bring great difficulties to the detection of infrared small targets.A multi-channel based on attention network is proposed in this paper,aimed at the problem of high missed detection rate and false alarm rate of traditional algorithms and the problem of large model,high complexity and poor detection performance of deep learning algorithms.First,given the difficulty in extracting the features of infrared multiscale and small dim targets,the multiple channels are designed based on dilated convolution to capture multiscale target features.Second,the coordinate attention block is incorporated in each channel to suppress background clutters adaptively and enhance target features.In addition,the fusion of shallow detail features and deep abstract semantic features is realized by synthesizing the contextual attention fusion block.Finally,it is verified that,compared with other state-of-the-art methods based on the datasets SIRST and MDFA,the proposed algorithm further improves the detection effect,and the model size and computational complexity are smaller.

局部对比度融合重加权的红外图像小目标检测

红外图像块小目标检测算法在数据链、预警、制导等领域有较为广泛的应用,如利用数据链可以将红外图像块小目标精确传送给雷达。为了在复杂背景条件下进一步提高红外小目标检测的效果,提出了一种重加权和局部先验融合的红外图像块(IPI)小目标检测算法。首先,采用加权Schatten p范数对背景图像块进行约束;其次,引入局部对比度先验信息和加权l_(1)范数抑制非目标稀疏点,进一步增强目标图像稀疏性,使算法模型性能进一步得到提升。仿真结果表明,所提算法在抑制背景杂波和精确检测目标方面均有较好的结果,优于现有经典算法。

基于信息补偿的红外弱小目标检测方法

针对红外弱小目标容易在网络迭代过程中损失纹理细节信息,从而导致目标定位和轮廓分割的准确性下降的问题,提出一种基于信息补偿的红外弱小目标检测方法。首先,利用图像特征提取(IFE)模块编码红外源图像的浅层细节及深层语义特征;其次,构建多级信息补偿(MIC)模块通过聚合相邻级别的特征对编码阶段下采样后的特征进行信息补偿;随后,引入全局目标响应(GTR)模块联合特征图的全局上下文信息对卷积局部性的限制进行补偿;最后,构建非对称交叉融合(ACF)模块对浅层和深层特征进行融合,以实现目标解码时纹理信息与位置信息的保留,进而完成对红外弱小目标的检测。在公开的NUAA-SIRST(Nanjing University of Aeronautics and Astronautics-Singleframe InfraRed Small Target)和NUDT-SIRST(National University of Defense Technology-Single-frame InfraRed Small Target)混合数据集上训练和测试的实验结果表明,与UIUNet(U-Net in U-Net Network)、LSPM(Local Similarity Pyramid Modules)和DNANet(Dense Nested Attention Network)等方法相比,所提方法在交并比(IoU)上分别提高了9.2、8.9和5.5个百分点,在F1分数(F1-Score)上分别提高了6.0、5.4和3.1个百分点。以上表明所提方法对红外复杂背景图像中的弱小目标可以实现准确检测和有效分割。

基于海思Hi3531部署的红外小目标检测算法研究

针对现有算法计算量大、实时性差、部署困难等问题,同时为满足红外探测系统对实时性及准确率的高要求,提出了一种部署于国产嵌入式芯片的轻量化算法,即YOLOv5-Tiny Hisi. YOLOv5-Tiny Hisi算法根据红外小目标特点对主干网络结构进行轻量化改造,并使用SIo U优化损失函数中的边界误差,提高了红外小目标定位的准确性.将YOLOv5-Tiny Hisi算法模型部署到海思Hi3531DV200嵌入式开发板上,利用芯片集成的神经网络加速引擎(neural network inference engine, NNIE)对网络推理进行加速.在公开数据集上的实验结果表明,该算法能够大幅度降低参数量和模型大小,与YOLOv5相比,在平均精度上的提升了1.52%.在海思Hi3531DV200嵌入式开发板上对分辨率为(1 280×512)像素的单张图像推理速度可达到35帧/s,召回率可达到95%,满足了红外探测系统对实时性和准确率的要求.

自注意卷积融合的嵌入式平台红外小目标检测

针对嵌入式硬件平台下红外小目标检测存在的内存与计算资源受限问题,高帧率检测需求,以及更高的目标级检测性能要求,提出了一种名为CAMNet的检测网络。该网络结合自注意力全局建模的优势与卷积轻量快速的处理特性,采用四级堆叠的编码器和解码器架构,有效降低了算法资源需求,提升了检测帧率;同时在损失函数方面提出了质心损失函数,有效提升了算法的目标级检测性能。在公开的SIRST数据集上的实验结果显示,CAMNet在常见嵌入式平台的检测帧率达107 FPS,相比于ISTDU-Net、UIU-Net等其它先进网络,目标检测率至少提高了0.76%,虚警率至少降低了87.30%,表明所提检测网络具备较快的检测速度以及较好的目标级检测性能。

An Effective Method of Threshold Selection for Small Object Image

The image segmentation difficulties of small objects which are much smaller than their background often occur in target detection and recognition. The existing threshold segmentation methods almost fail under the circumstances. Thus, a threshold selection method is proposed on the basis of area difference between background and object and intra-class variance. The threshold selection formulae based on one-dimensional (1-D) histogram, two-dimensional (2-D) histogram vertical segmentation and 2-D histogram oblique segmentation are given. A fast recursive algorithm of threshold selection in 2-D histogram oblique segmentation is derived. The segmented images and processing time of the proposed method are given in experiments. It is compared with some fast algorithms, such as Otsu, maximum entropy and Fisher threshold selection methods. The experimental results show that the proposed method can effectively segment the small object images and has better anti-noise property.

基于YOLOv5s的改进实时红外小目标检测

针对红外图像分辨率低、背景复杂、目标细节特征缺失等问题,提出了一种基于YOLOv5s的改进实时红外小目标检测模型Infrared-YOLOv5s。在特征提取阶段,采用SPD-Conv进行下采样,将特征图切分为特征子图并按通道拼接,避免了多尺度特征提取过程中下采样导致的特征丢失情况,设计了一种基于空洞卷积的改进空间金字塔池化模块,通过对具有不同感受野的特征进行融合来提高特征提取能力;在特征融合阶段,引入由深到浅的注意力模块,将深层特征语义特征嵌入到浅层空间特征中,增强浅层特征的表达能力;在预测阶段,裁减了网络中针对大目标检测的特征提取层、融合层及预测层,降低模型大小的同时提高了实时性。首先通过消融实验验证了提出各模块的有效性,实验结果表明,改进模型在SIRST数据集上平均精度均值达到了95.4%,较原始YOLOv5s提高了2.3%,且模型大小降低了72.9%,仅为4.5 M,在Nvidia Xavier上推理速度达到28 f/s,利于实际的部署和应用。在Infrared-PV数据集上的迁移实验进一步验证了改进算法的有效性。提出的改进模型在提高红外图像小目标检测性能的同时,能够满足实时性要求,因而适用于红外图像小目标实时检测任务。

基于跨越连接与融合注意力机制的红外弱小目标检测方法

针对复杂背景红外小弱目标信号弱、特征不明显、干扰虚警多等检测性能低问题,提出基于跨越连接与融合注意力机制的单阶段红外弱小目标检测算法。该方法融合注意力机制与残差网络提取小目标多特征,减少复杂背景干扰;双向跨越连接结构融合低层与高层各自的特征信息,凸显小弱目标特征表达能力;增加一个高分辨率检测层,重新聚类弱小目标先验框,增强目标与背景的特征差别学习能力;最后,建立真实目标和预测目标框的高斯分布模型,计算两者相似性,解决因IoU度量造成的目标损失回归偏差敏感问题,提升损失回归准确性。在公开红外小目标数据集上进行对比测试,实验结果表明该算法对多种复杂背景下红外小弱目标检测均取得了最佳性能,在平均精度和速度等方面都得到显著提升,模型最小,方便部署。

基于渐进时空特征融合的红外弱小目标检测

为避免现有多帧红外弱小目标检测算法在显式对齐多帧特征时产生的估计误差累积,并缓解网络降采样导致的目标特征丢失,提出了一种渐进时空特征融合网络,采用渐进时序特征累积模块隐式地聚合多帧信息,并利用多尺度空间特征融合模块增强浅层细节特征与深层语义特征之间的交互。针对多帧红外弱小目标数据集稀缺的现状,构建了一个高度真实的半仿真数据集。与主流算法相比,提出的算法在所提出数据集和公开数据集上的检测概率分别提升了4.69%与4.22%。

HRformer:基于多级回归Transformer网络的红外小目标检测

红外小目标检测是指从低信噪比、复杂背景的红外图像中对小目标进行检测,在海上救援、交通管理等应用中具有重要实际意义。然而,由于图像分辨率低、目标尺寸小以及特征不突出等因素,导致红外目标很容易淹没在包含噪声和杂波的背景中,如何精确检测红外小目标的外形信息仍然是一个挑战。针对上述问题,构建了一种基于多级回归Transformer(HRformer)网络的红外小目标检测算法。具体来说,首先为了在获得多尺度信息的同时尽可能避免原始图像信息的损失,采用像素逆重组(PixelUnShuffle)操作对原始图像下采样来获取不同层级网络的输入,同时采用一种可学习的像素重组(PixelShuffle)操作对每一层级的输出特征图进行上采样,提升了网络的灵活性;接着,为实现网络中不同层级特征之间的信息交互,本文设计了一种包含空间注意力计算分支以及通道注意力计算分支在内的交叉注意力融合(cross attention fusion,CAF)模块实现特征高效融合以及信息互补;最后,为进一步提升网络的检测性能,结合普通Transformer结构具有较大感受野以及基于窗口的Transformer结构具有较少计算复杂度的优势,提出了一种局部-全局Transformer(LGT)结构,能够在提取局部上下文信息的同时对全局依赖关系进行建模,计算成本也得到节省。实验结果表明,与目前较为先进的一些红外小目标检测算法相比,本文所提出的算法具有更高的检测精度,同时具有较少的参数量,在解决实际问题中更有意义。

基于YOLOv7-EPAN的光伏板红外图像缺陷检测

光伏板是光伏电站重要组成部件,需定期对其进行检测,保证光伏电站安全运行。针对航拍光伏图像复杂背景下小目标难检测的问题,提出一种基于YOLOv7-EPAN的光伏板红外图像缺陷检测方法。首先提出融合CSWin Transformer的扩展高效网络CS-ELAN模块,捕获全局有效信息抑制背景信息;其次以CS-ELAN为基础构建高效路径特征聚合网络EPAN(Efficient path aggregation characteristic pyramid network),加强不同特征层的信息交互,丰富语义特征信息,提高特征表达能力;最后优化损失函数,使模型关注高质量先验框,提高小目标定位精度。在航拍光伏红外数据集上进行实验,结果表明:相比于原YOLOv7模型,所提方法的mAP50、mAP50:95分别提高了6.4%、3.3%,表明所提方法能较好地解决航拍光伏图像复杂背景下小目标缺陷漏检的问题。

多尺度注意力特征增强融合的红外小目标检测新网络

为提高红外成像中小目标检测的性能,提高低空空域监管能力,提出一种基于多尺度注意力特征增强融合的红外小目标检测新网络。首先,使用Resnet34提取红外图像的多尺度特征;其次,使用多尺度空间注意力特征增强模块(MFEM)来提高特征提取能力;然后,在逐级上采样过程中使用双通道注意力特征融合模块(DFFM),融合语义信息和细节信息,以更好地保护红外小目标的特征;最后,与其他方法对比,并以地/空红外弱小飞机目标视频序列检测为例测试真实场景。结果表明:新方法与现有方法相比,交互比(IoU)、F值和漏检率(FNR)的评分均获得改进;通过多尺度注意力特征增强融合可准确地定位到目标并生成精细的分割结果;MFEM能够同时利用多尺度上下文信息和空间注意力机制来突出红外小目标;DFFM通过给不同通道特征的集合赋予权重,得到最合适的特征图进行特征融合,从而提高检测性能。