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 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.

Development of small pixel HgCdTe infrared detectors

After approximately half a century of development, HgCdTe infrared detectors have become the first choice for high performance infrared detectors, which are widely used in various industry sectors, including military tracking, military reconnaissance, infrared guidance, infrared warning, weather forecasting, and resource detection. Further development in infrared applications requires future HgCdTe infrared detectors to exhibit features such as larger focal plane array format and thus higher imaging resolution. An effective approach to develop HgCdTe infrared detectors with a larger array format size is to develop the small pixel technology. In this article, we present a review on the developmental history and current status of small pixel technology for HgCdTe infrared detectors, as well as the main challenges and potential solutions in developing this technology. It is predicted that the pixel size of long-wave HgCdTe infrared detectors can be reduced to5 μm, while that of mid-wave HgCdTe infrared detectors can be reduced to 3 μm. Although significant progress has been made in this area, the development of small pixel technology for HgCdTe infrared detectors still faces significant challenges such as flip-chip bonding, interconnection, and charge processing capacity of readout circuits. Various approaches have been proposed to address these challenges, including three-dimensional stacking integration and readout circuits based on microelectromechanical systems.

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.

A pixel-level local contrast measure for infrared small target detection

Infrared(IR) small target detection is one of the key technologies of infrared search and track(IRST)systems. Existing methods have some limitations in detection performance, especially when the target size is irregular or the background is complex. In this paper, we propose a pixel-level local contrast measure(PLLCM), which can subdivide small targets and backgrounds at pixel level simultaneously.With pixel-level segmentation, the difference between the target and the background becomes more obvious, which helps to improve the detection performance. First, we design a multiscale sliding window to quickly extract candidate target pixels. Then, a local window based on random walker(RW) is designed for pixel-level target segmentation. After that, PLLCM incorporating probability weights and scale constraints is proposed to accurately measure local contrast and suppress various types of background interference. Finally, an adaptive threshold operation is applied to separate the target from the PLLCM enhanced map. Experimental results show that the proposed method has a higher detection rate and a lower false alarm rate than the baseline algorithms, while achieving a high speed.

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.

Design of Diode Type Un-Cooled Infrared Focal Plane Array Readout Circuit

The diode infrared focal plane array uses the silicon diodes as a sensitive device for infrared signal measurement. By the infrared radiation, the infrared focal plane can produces small voltage signals. For the traditional readout circuit structures are designed to process current signals, they cannot be applied to it. In this paper, a new readout circuit for the diode un-cooled infrared focal plane array is developed. The principle of detector array signal readout and small signal amplification is given in detail. The readout circuit is designed and simulated by using the Central Semiconductor Manufacturing Corporation （CSMC） 0.5 μm complementary metal-oxide-semiconductor transistor （CMOS） technology library. Cadence Spectre simulation results show that the scheme can be applied to the CMOS readout integrated circuit （ROIC） with a larger array, such as 320×240 size array.

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.

Study on the Authenticity of Identification Methods in White Spirit by Infrared Spectrometer

This experiment studies on the used infrared spectroscopy to establish technology methods for liquor identification methods, as well as offers the science data for establishment of the fingerprint in white spirit. The results have shown that using near-infrared spectroscopy analysis of liquor has the obvious features such as strong specificity, good reproducibility, simple operation, and finally confirmed that it is an authentic and ideal method for identification in white spirit.

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.

A Small Target Detection Method for Sea Surface Based on Guided Filtering and Local Mean Gray Difference

The traditional small target detection algorithm often results in a high false alarm rate on the sea surface background. To address this issue, a small target detection method based on guided filtering and local average gray level difference was proposed in this paper for the sea surface. Firstly, the method enhanced the details of the small targets by employing guided filtering to suppress the background clutter and noise in the sea surface image. Subsequently, the local average gray level difference of each point in the image was calculated to further distinguish the targets from other interference points. Finally, the threshold segmentation method was utilized to obtain the actual small targets on the sea surface. After conducting experiments on various sea surface scenes, the LSCRG, BSF, and ROC curve were computed for the proposed method and five other algorithms. Comparative analysis with BS, Top-hat, TDLMS, Max-median, and LCM demonstrates the superiority of the proposed method for infrared small target detection on the sea surface.

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。

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。

一种多深度特征连接的红外弱小目标检测方法

针对红外弱小目标像元数量少、图像背景复杂、检测精度低且耗时较长的问题,文中提出了一种多深度特征连接的红外弱小目标检测模型(MFCNet)。首先,提出了多深度交叉连接主干形式以增加不同层间的特征传递,增强特征提取能力;其次,设计了注意力引导的金字塔结构对深层特征进行目标增强,分离背景与目标;提出非对称融合解码结构加强解码中纹理信息与位置信息保留;最后,引入点回归损失得到中心坐标。所提网络模型在SIRST公开数据集与自建长波红外弱小目标数据集上进行训练并测试,实验结果表明,与现有数据驱动和模型驱动算法相比,所提算法在复杂场景下具有更高的检测精度及更快的速度,模型的平均精度相比次优模型提升了5.41%,检测速度达到100.8 FPS。

硬脂酸亚甲基面外弯曲振动变温FT-IR光谱研究

在293～393 K温度范围内,分别采用傅里叶红外光谱、二阶导数红外光谱、四阶导数红外光谱及去卷积红外光谱测定硬脂酸亚甲基面外弯曲振动(ωCH2),来进一步研究温度对于硬脂酸脂肪链构象的影响。实验发现:在1180～1320cm-1范围内,硬脂酸ωCH2存在"第一特征谱带"和未见文献报道的"第二特征谱带"。硬脂酸脂肪链由全反式有序构象到无序构象的改变的临界温度为353～358K,并进一步研究了温度对于硬脂酸ωCH2红外吸收峰强度的影响。

基于纳米铂黑材料的超高辐射率MEMS红外光源研究

本文基于微机电系统(MEMS)设计和加工技术,研制了一种高辐射率、低功耗、高调制速率的MEMS红外(IR)光源,通过在工艺中引入高可靠的纳米铂黑(Pt-black)材料作为红外辐射层,在2~14μm光谱范围内成功实现了99.5%以上的平均辐射效率。在5V驱动电压下,该MEMS红外光源的辐射区温度达到407℃,响应时间(T90)为17ms,在10Hz和100Hz工作频率下的调制深度分别达到100%和42%。最后,把该MEMS红外光源芯片集成在一种微型二氧化碳(CO_(2))气体传感器样机中进行了实验验证,结果表明:该器件具备良好的辐射特性和温度特性。该MEMS红外光源可以用于常规的CO_(2)和甲烷(CH_(4))气体检测,以及二氧化硫(SO_(2))、硫化氢(H_(2)S)、六氟化硫(SF_(6))、氨气(NH_(3))等多种工业气体检测。

CMOS红外光源的设计与实现

基于红外气体传感器微型化、低成本和低功耗的发展需要,设计了一种用于非色散红外(NDIR)集成气体传感器的互补金属氧化物半导体(CMOS)红外光源。它以非等间隔的蛇形钨(W)薄膜电阻作为加热丝,以二氧化硅(SiO_(2))和氮化硅(Si_(3)N_(4))多层复合介质薄膜为支撑形成悬空膜片式微热板,以氧化铜(CuO)和二氧化锰(MnO_(2))纳米材料复合薄膜作为辐射增强层。基于COMSOL软件进行了热电耦合仿真,证明结构设计合理性。采用标准CMOS工艺、硅(Si)的深刻蚀工艺以及静电流体动力学打印技术流片制造了该CMOS红外光源芯片。性能测试结果表明:该红外光源从室温升温至469℃的热响应时间约为41 ms,电功耗仅为138 mW,辐射区温度分布均匀,引入辐射增强层使表面比辐射率提高约35%,红外光源的辐射功率和红外光谱辐射强度测试结果表明:该涂层有效地增强了红外辐射。

FT-IR光谱法测定籼米淀粉回生

研究了傅立叶变换红外(FT-IR)光谱法快速检测籼米淀粉回生,发现FT-IR光谱中一些特征振动模式的相对强度(与761.76 cm-1处峰高的比值)会随淀粉回生程度的增加而降低.振动模式包括:3 415 cm-1附近的O—H伸缩振动、2 927.46 cm-1处的CH2伸缩振动、1 155.17 cm-1处C—O和C—C伸缩振动、1 081.89 cm-1处的C—O—H弯曲振动、1 043.32,1 020.18和998.96 cm-1处未确定的振动模式、609.41和578.55 cm-1处的吡喃环骨架振动模式.将以上这些特征振动模式的相对强度倒数与DSC回生焓值进行相关性分析可知:1 081.89,1 043.32,1 020.18,609.41和578.55 cm-1处吸收峰的相关系数均能达到0.9以上,因此可将这5种FT-IR振动模式的相对强度变化作为定量指示淀粉回生程度的多重指标.