Research on Infrared Image Fusion Technology Based on Road Crack Detection

This study aimed to propose road crack detection method based on infrared image fusion technology.By analyzing the characteristics of road crack images,this method uses a variety of infrared image fusion methods to process different types of images.The use of this method allows the detection of road cracks,which not only reduces the professional requirements for inspectors,but also improves the accuracy of road crack detection.Based on infrared image processing technology,on the basis of in-depth analysis of infrared image features,a road crack detection method is proposed,which can accurately identify the road crack location,direction,length,and other characteristic information.Experiments showed that this method has a good effect,and can meet the requirement of road crack detection.

Thermal Infrared Salient Human Detection Model Combined with Thermal Features in Airport Terminal

Target detection in low light background is one of the main tasks of night patrol robots for airport terminal.However,if some algorithms can run on a robot platform with limited computing resources,it is difficult for these algorithms to ensure the detection accuracy of human body in the airport terminal. A novel thermal infrared salient human detection model combined with thermal features called TFSHD is proposed. The TFSHD model is still based on U-Net,but the decoder module structure and model lightweight have been redesigned. In order to improve the detection accuracy of the algorithm in complex scenes,a fusion module composed of thermal branch and saliency branch is added to the decoder of the TFSHD model. Furthermore,a predictive loss function that is more sensitive to high temperature regions of the image is designed. Additionally,for the sake of reducing the computing resource requirements of the algorithm,a model lightweight scheme that includes simplifying the encoder network structure and controlling the number of decoder channels is adopted. The experimental results on four data sets show that the proposed method can not only ensure high detection accuracy and robustness of the algorithm,but also meet the needs of real-time detection of patrol robots with detection speed above 40 f/s.

Utilization of Thermal Infrared Image for Inversion of Winter Wheat Yield and Biomass

The present paper utilizes thermal infrared image for inversion of winter wheat yield and biomass with different technology of irrigation(drip irrigation,sprinkler irrigation,flood irrigation).It is the first time that thermal infrared image is used for predicting the winter wheat yield and biomass.The temperature of crop and background was measured by thermal infrared image.It is necessary to get the crop background separation index(CBSIL,CBSIH),which can be used for distinguishing the crop value from the image.CBSIL and CBSIH(the temperature when the leaves are wet adequately;the temperature when the stomata of leaf is closed completely) are the threshold values.The temperature of crop ranged from CBSIL to CBSIH.Then the ICWSI was calculated based on relevant theoretical method.The value of stomata leaf has strong negative correlation with ICWSI proving the reliable value of ICWSI.In order to construct the high accuracy simulation model,the samples were divided into two parts.One was used for constructing the simulation model,the other for checking the accuracy of the model.Such result of the model was concluded as:(1) As for the simulation model of soil moisture,the correlation coefficient(R2) is larger than 0.887 6,the average of relative error(Er) ranges from 13.33% to 16.88%;(2) As for the simulation model of winter wheat yield,drip irrigation(0.887 6,16.89%,-0.12),sprinkler irrigation(0.970 0,14.85%,-0.12),flood irrigation(0.969 0,18.87%,0.18),with the values of R2,Er and CRM listed in the parentheses followed by the individual term.(3) As for winter wheat biomass,drip irrigation(0.980 0,13.70%,0.13),sprinkler irrigation(0.95,13.15%,-0.14),flood irrigation(0.970 0,14.48%,-0.13),and the values in the parentheses are demonstrated the same as above.Both the CRM and Er are shown to be very low values,which points to the accuracy and reliability of the model investigated.The accuracy of model is high and reliable.The results indicated that thermal infrared image can be used potentially for inversion of winter wheat yield and biomass.

INFRARED THERMAL IMAGE STUDY ON THE FOREWARNING OF COAL AND SANDSTONE FAILURE UNDER LOAD

In the experimental study, AGE-782 thermal instrument was used to detect the infrared radiation variation of coal and sandstone (wave-length range 3.6～5.5 μm was used). It's discovered that coal and sandstone failure under load have three kinds of infrared thermal features as well as infrared forewarning messages. That are: (1) temperature rises gradually but drops before failure ; (2) temperature rises gradually but quickly rises before failure; (3) first rises,then drops and lower temperature emerges before failure. The further researches and the prospect of micro-wave remote sensing detection .on ground pressure is also discussed.

Design of Uncooled Thermal Imaging Systems Operating at Multiple Temperatures

The reasons why thermal imaging systems consume power are analyzed,and a low power consumption design scheme is presented for the thermal imaging systems operating at multiple temperatures. The relation between the response performance of α-Si microbolometer detector and its operating temperature is studied by means of formulas of microbolometer detector's noise equivalent temperature difference(NETD) and detectivity. Numerical analysis based on true parameters demonstrates that the detectivity decreases slightly and NETD increases slightly when operating temperature rises,which indicates that α-Si microbolometer detector has approximately uniform response in a wide operating temperature range. According to these analyses,a thermal imaging system operating at multiple temperatures is designed. The power of thermoelectric stabilizer(TEC) is less than 350 mW and NETD is less than 120 mK in the ambient temperature range of-40 ℃-60 ℃,which shows that this system not only outputs high-quality images but consumes low power.

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.

基于红外热成像的围护结构热工性能定量检测方法研究进展

综述红外热成像技术在建筑围护结构热工性能定量检测领域的研究成果,介绍国内外定量红外热成像法的理论基础、发展历程以及在量化热工缺陷能量损失中的应用,从构建稳态热流环境、对流辐射换热计算、测试操作选择3个方面总结现场检测的影响因素,归纳图像处理技术在排除光学干扰、辨识热工缺陷、热工信息可视化等方面对红外检测领域的贡献,分析未来红外热成像的应用前景与发展方向。研究表明,红外热成像技术可成为围护结构热工性能定量检测与热工缺陷量化识别的优秀方法。

深度学习与图像融合的行人检测算法研究

为解决智能辅助驾驶技术中可见光摄像机受光照和气候影响而导致行人目标识别困难的问题。通过研究图像融合技术,结合深度卷积神经网络,实现并改进了一种道路行人目标检测算法。方法是利用多源传感器图像融合技术,采用可见光相机与红外热成像相机融合的策略,以Faster RCNN算法为基础,从改进网络结构、特征融合、优化模型训练等方面展开研究,对复杂环境下的行人检测与定位跟踪展开研究,提出一种基于图像融合技术和改进的深度卷积神经网络的道路行人目标检测算法。实验结果表明,该算法对复杂气候环境下行人目标检测提高了检测效率和准确率,增加了智能辅助驾驶汽车的安全性。

基于对比学习的改进SSD目标检测算法

现有基于深度学习的目标检测算法在图像的目标检测过程中存在物体视角的多样性、目标本身形变、检测物体受遮挡、光照性以及小目标检测等问题。为了解决这些问题,本文将对比学习思想引入到SSD(Single Shot MutiBox Detectior)目标检测网络中,对原有的SSD算法进行改进。首先,通过采用图像截块的方式随机截取样本图片中的目标图片与背景图片,将目标图像块与背景图像块输入到对比学习网络中提取图片特征进行对比损失计算。随后,使用监督学习的方法对SSD网络进行训练,将对比损失传入到SSD网络中与SSD损失值加权求和反馈给SSD网络,进行网络参数的优化。由于在目标检测网络中加入了对比学习的思想,提高了背景和目标在特征空间中的区分度。因此所提出的算法能显著提高SSD网络对于目标检测的精度,并在可见光和热红外图像中均取得了令人满意的检测效果。在PASCAL VOC2012数据集实验中,AP50值提升了0.3%,在LLVIP数据集实验中,AP50值提升了0.2%。

基于红外图像的笼养白羽肉鸡体温检测方法

【目的】针对大规模笼养肉鸡体温自动检测困难的问题,提出一种深度学习与回归分析相结合的肉鸡体温检测方法。【方法】采用红外热成像仪采集肉鸡的红外图像,通过YOLOv5s深度学习算法训练感兴趣区域(肉鸡鸡头)模型,分别引入多元线性回归和多元非线性回归以建立肉鸡感兴趣区域温度与翅下温度的预测模型,从而实现自动体温检测。【结果】基于深度学习的感兴趣区域模型的精准率与召回率分别为93.8%和95.8%,多元线性回归温度预测模型1和多元非线性回归温度预测模型的平均相对误差分别为0.28%和0.27%,温度预测值与真实值的最大差值分别为0.34和0.32℃。【结论】非线性模型预测肉鸡体温的准确率更高,可为日后舍内鸡只温度自动巡检设备提供技术支撑及前期研究基础。

红外热像技术辅助下探讨中药薰药治疗风寒湿痹型膝骨关节炎的临床研究

目的:观察中药熏药治疗风寒湿痹型膝骨关节炎(Knee Osteoarthritis,KOA)患者的临床疗效,并采用医用红外热成像仪进行疗效系统评价。方法:根据随机数字表法将60例风寒湿痹型KOA患者分为治疗组与对照组,对照组予口服艾瑞昔布片,治疗组在对照组治疗的基础上配合柳州市中医医院中药验方进行膝部中药熏药治疗。治疗8周后,比较两组治疗前后膝周疼痛视觉模拟评分法(Visual Analogue Scale,VAS)评分、西安大略和麦克马斯特大学骨关节炎指数量表(Western Ontario and McMaster Universities Osteoarthritis Index,WOMAC)评分、超敏C反应蛋白、红细胞沉降率、经络穴位皮温等的变化情况。结果:治疗组与对照组VAS评分和WOMAC评分及炎症因子水平较治疗前均显著降低(P<0.01),治疗组VAS评分和WOMAC评分及炎症因子水平均低于对照组(P<0.01);两组治疗后主要穴位均温显著高于治疗前(P<0.01),且治疗组较对照组的均温改善显著(P<0.01);治疗组愈显率显著高于对照组,两组患者的愈显率进行比较,P<0.05。结论:中药验方熏药治疗方案能改善风寒湿痹型KOA患者的证候和有效缓解疼痛,显著降低KOA评分和提高相应经络穴位的红外皮温,并通过红外热像技术得到证实,可为临床适宜技术推广与应用提供可靠的临床数据和值得借鉴的研究思路。

基于LabVIEW的红外热波图像缺陷检测系统设计

碳纤维增强复合材料大量应用于航空领域,对其质量提出了更高的要求。但是传统人工检测方法工作强度高、效率低。为了提高碳纤维复合材料的缺陷检测效率,本文基于LabVIEW软件开发平台设计CFRP(carbon fiber reinforced plastics)缺陷检测系统,提取缺陷边缘并进行数量统计。本研究采用主动式红外热成像无损检测技术,通过红外热像仪获取激光扫描的损伤试样表面热图像。针对红外图像对比度及均匀性差的特点,使用HSL(hue, saturation, luminance)进行颜色平面提取,灰度变换,选用适用于处理光照分布不均匀图像的Niback局部阈值分割处理算法进行感兴趣区域图像阈值分割处理。最后通过形态学处理增强图像并实现缺陷特征提取和缺陷数量统计。本文通过搭建红外热成像缺陷检测实验平台完成红外热波缺陷图像的采集、处理,设计软件平台及用户界面以实现缺陷特征的提取。相比于人工检测,该系统的设计极大地减少了检测用时,有助于实现缺陷检测的自动化。

基于电磁波的煤岩识别技术研究进展

将电磁波应用于煤岩识别中,可有效提高煤岩界面分辨能力。结合煤岩界面模型,阐述了运用电磁波技术进行煤岩识别的原理;介绍了γ射线法、雷达探测法、太赫兹信号法、电子共振法、X射线法和红外热成像法6种具体的煤岩识别方法,分析了各方法的原理,对各方法的优缺点及煤矿井下适用性进行了对比,并结合实际工业应用分析了各方法的研究现状。γ射线法在探测距离上具有显著优势,但存在放射性问题,基本被淘汰;雷达探测法具有识别准确的优点,但由于其信号衰减严重,探测距离短,目前一般应用于薄煤层测厚;太赫兹信号法探测距离短,只有在井下环境组成稳定时才能应用;电子共振法信号衰减严重,探测距离较短且难度较大,所以目前矿井基本摒弃;X射线法穿透性强,成像较清晰,但危害性极大;红外热成像法中,主动红外激励法需耗费大量时间对煤岩进行激励,且在处于高瓦斯的矿井环境中,存在极大的安全隐患;截割闪温法虽耗时较短,但对于截齿多、排布复杂的情况很难实现有效的煤岩识别。指出电磁波回波信息决定着电磁波煤岩识别的准确性,后续应对其进行深层次挖掘。

基于红外热成像技术探究八段锦对平人八会穴的影响

目的:观察习练八段锦对平人八会穴中的章门穴、中脘穴、膻中穴、膈俞穴穴位温度的影响。方法:招募健康受试者40名,采用随机数字表法将受试者分为观察组与对照组,每组20名。观察组习练八段锦,对照组习练第九套广播体操,对纳入的受试者进行为期12周的观察,分别于入组后习练前、习练后即刻、习练12周后采用FLIR SC660型红外热像仪观察受试者左右章门穴、中脘穴以及膻中穴、膈俞穴的温度变化。结果:观察组与对照组习练后即刻章门穴、中脘穴、膻中穴、膈俞穴温度上升,但差异无统计学意义(P>0.05),习练12周后2组差异有统计学意义(P<0.05)。结论:八段锦可通过刺激平人脏、腑、气、血之精气,一定程度提高其章门穴、中脘穴、膻中穴、膈俞穴的温度,且长期习练效果更佳,为八段锦“协调脏腑,促进气血运行”提供客观化、可视化依据。

基于红外图像技术的运行设备热故障状态检测系统设计

变电站设备在发电过程中因老化与恶劣环境等因素的影响容易发生热故障,传统人工检测方法难度大且准确性低,提出基于红外图像的变电站设备热故障状态检测系统设计。硬件设计上,由红外图像采集模块与地面服务模块组成系统整体架构。软件设计上,采用串并联扫描方式采集变电站设备的红外图像,通过均值滤波法与直方图均衡化算法预处理原始采集的红外图像,基于红外图像的相对温差法完成热故障状态检测。最后通过对变电站常见设备进行热故障状态检测试验,验证了设计系统的可行性与可靠性。

基于深度学习的管道热图像泄漏识别

为了降低输液管道多泄漏点微小泄漏的检测难度,提高输液管道无损检测的检测精度与检测速度,通过搭建水循环管道泄漏实验系统,改变管道泄漏点尺寸、泄漏点数量及输送介质温度,应用红外热像仪实时采集红外图像,提出基于非线性平稳小波和双边滤波算法实现图像降噪;并结合红外检测技术和YOLO(You Only Look Once)v4模型实现输液管道单、多漏点的自动化智能检测。结果表明,与传统滤波算法相比,该降噪方法的峰值信噪比、结构相似性均有所提升;该模型能够快速且准确地检测管道单、多漏点,检测精度(mAP)分别达到了0.9822及0.98,准确率分别达到了98.3%及98.36%,单帧检测时间分别达到了0.3021 s及0.3096 s,实现了在复杂背景干扰下对单、多泄漏点的识别。通过与YOLO v3、Faster R-CNN和SSD 300这3种算法比较发现,YOLO v4算法对管道单一漏点及多泄漏点检测的准确率、mAP和检测时间均更佳,具有更高的检测准确性与检测效率。

基于低对比度红外图像时空信息的气体泄漏检测

气体泄漏事故造成的危害是多方面的,如环境污染、人员财产损失、火灾爆炸。红外热成像作为可实现大范围快速成像的定性探测技术被广泛使用,相比一般红外图像,气体红外图像的对比度更低,边缘更加模糊,不易识别。针对上述问题,本文提出一种基于混合高斯背景建模的低对比度气体红外图像泄漏检测方法。首先,在预处理阶段,提出时域自适应帧间滤波算法实现红外图像的降噪和细节保持;然后,基于空域信息和梯度信息约束,提出时空混合高斯背景模型实现泄漏气体目标的前景的初步提取;最后,为更好地去除前景检测中干扰的运动目标,利用改进的快速鲁棒的模糊C均值聚类方法实现气体区域的自适应分割。实验结果表明,在5 m的泄漏距离下,该检测算法可有效提高准确率,弥补气体区域空洞问题,降低其他运动物体的干扰,气体泄漏检测准确率在92.3%~96.3%,与其他算法相比具有显著的抗干扰和区域分割能力。

基于红外热成像的地铁隧道渗漏水病害智能识别

为了准确、高效检测出隧道渗漏水病害,本文提出利用红外热成像技术采集图像数据,采用结合MobileNet V2的U-Net模型进行地铁隧道渗漏水病害智能识别的方法。结合MobileNet V2的U-Net模型既继承了MobileNet V2的轻量化优势,又保留了U-Net在小样本数据集上良好的分割功能。将从我国南方一地铁隧道内采集到的878张红外热成像图作为原始数据,对数据进行分割标注预处理后,建立标签为地铁隧道渗漏水的数据集。将数据集按9∶1的比例划分为训练集和测试集,使用本文方法进行渗漏水病害识别。结果表明:在保证识别精度的同时,计算参数量仅为原来的1/18,大幅降低了运算量;对小面积点状、中等面积条状和大面积复杂形状三类隧道渗漏水病害均有较好的识别效果;渗漏水区域和周围暗角均为蓝色时,采用传统数字图像处理方法比较容易受到暗角区域影响,而采用本文方法可以较准确识别渗漏水区域,说明本文方法识别效果优于传统数字图像处理方法,值得推广。

一种发射率可矫正的红外热成像全局检测方法

在无损检测领域中,红外热成像检测具有重要地位。在利用红外热成像技术检测缺陷的过程中,受加工工艺和周围环境的影响,待检测设备的表面粗糙度各有殊异,材料表面的发射率不均,导致温度采集误差较大。针对该问题,结合热辐射基本定律探究出一种发射率可矫正的红外热成像全局检测法,意在矫正材料的表面发射率,降低检测过程中可能出现的缺陷错检漏检等安全性问题的风险。其仿真和实验结果表明,一方面使用红外热成像法检测时,表面粗糙度不同会严重干扰检测的准确性,具体影响为粗糙度越大,检测出的辐射温度越高;另一方面使用分离温度法可以实现矫正物体表面由于粗糙度不同带来的发射率不同的影响,仿真和实验结果表明矫正准确度可达70%左右,最高可以达到75%以上。基于此矫正检测方法能够有效实现基于红外热成像技术的全局检测,减少发射率对红外热像仪检测精度的影响,提高检测精度,同时也可以极大程度上提高缺陷检测的效率。

基于红外热成像检测的光伏电站异常分析

红外热成像检测具有非接触、检测速度快、检测范围广、易于实现自动化和可以实时观测等优点,在众多行业中都有广泛应用。为了更好地把红外热成像技术应用于光伏电站检测领域,利用手持式红外热成像仪和无人机搭载红外热成像仪两种检测方式,对多个在运光伏电站现场进行了红外热成像检测,根据红外热成像检测结果挑选出光伏电站常见的故障案例,然后分析了这些故障导致的异常红外热成像图的典型特征及形成原因。通过红外热成像检测结果发现:光伏组件和电气设备存在一些热异常问题,导致这些热异常的故障类型可分为5类,分别为:1)光伏组件中单片太阳电池热斑;2)光伏组件内太阳电池串开路;3)光伏组串(或光伏组件)短路或开路;4)光伏阵列完整性缺失或机械受损;5)电气设备异常。研究结果可以为红外热成像技术在光伏电站检测中的应用提供参考和借鉴,通过红外热成像图来准确识别光伏电站的故障类型,及时发现光伏电站存在的发热故障和隐患,从而可以提高光伏电站的运维效率,对促进光伏电站的安全高效运行和新能源行业的发展具有重要意义。