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河南工业大学学报（自然科学版） 2023年第1期44卷 104-111页

作者：鲁玉杰王文敬张俊东王争艳卢少华河南工业大学粮油食品学院河南郑州450001 江苏科技大学粮食学院江苏镇江212100

对粮食中隐蔽性害虫的早期诊断和检测,不仅可以减少因害虫取食造成的粮食产后损失,还可以减少化学药剂的使用,对于保证粮食品质和减少环境污染具有重要的意义。基于近红外光谱技术与极限学习机(ELM)构建小麦中不同生长阶段米象的分类识... 详细信息

对粮食中隐蔽性害虫的早期诊断和检测,不仅可以减少因害虫取食造成的粮食产后损失,还可以减少化学药剂的使用,对于保证粮食品质和减少环境污染具有重要的意义。基于近红外光谱技术与极限学习机(ELM)构建小麦中不同生长阶段米象的分类识别模型,采集未感染小麦和感染米象小麦的近红外光谱数据,选择SNV+De-trending的组合对原始光谱数据进行预处理,使用主成分分析(PCA)方法对光谱数据进行降维特征提取,利用ELM和支持向量机(SVM)建立分类识别模型。结果表明:ELM模型训练时间仅需0.062 5 s,总体分类准确率为90%,0、6、24和27 d的识别率为100%,10~20 d的幼虫期识别率偏低,20 d时识别率最低,为65%;SVM模型运行时间为3.38 s,分类准确率为85.42%,ELM模型较SVM模型的运行时间和分类准确率都有所提高。因此,ELM分类识别模型能够快速准确地判断小麦有无米象,以及分类识别小麦中不同生长发育阶段的米象。

关键词：近红外隐蔽性害虫极限学习机分类米象早期诊断

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Small infrared target detection by data-driven proposal and deep learning-based classification 44

Small infrared target detection by data-driven proposal and ...

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Conference on infrared technology and Applications XLIV

作者： Ryu, Junhwan Kim, Sungho Yeungnam Univ Dept Elect Engn 280 Daehak Ro Gyongsan 38541 Gyeongsangbuk D South Korea

ISBN: (纸本)9781510617605

In this paper, we propose a data-driven proposal and deep-learning based classification scheme for small target detection. Previous studies have shown feasible performance using conventional computer vision techniques such as spatial and temporal filters. However, those are handcrafted approaches and are not optimized due to the nature of the application fields. Recently, deep learning has shown excellent performance for many computer vision problems, which motivates the deep learning-based small target detection. The proposed data-driven proposal and convolutional neural network (DDP-CNN) can generate possible target locations through the data-driven proposal and final targets are recognized through the classification network. According to the experimental results using drone database, the DDP-CNN shows 91% of train accuracy and 0.85 of average precision (AP) of the target detection.

关键词： Small target detection infrared small target detection Deep learning classification Region proposal Convolution Neural Network

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基于改进YOLOv5s的红外船舶目标检测算法

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电光与控制 2024年第9期31卷 38-44页

作者：张琳薄敬东龚瑞昆崔传金华北理工大学电气工程学院河北唐山063000

针对传统船舶检测算法参数量多、计算量大的问题,提出一种结合知识蒸馏(KD)的轻量级红外船舶目标检测算法CB-YOLOv5。在YOLOv5s主干网络引入轻量级网络Ghost模型以实现检测主干网络的轻量化;引用新的颈部结构渐进型特征金字塔网络(AFPN)... 详细信息

针对传统船舶检测算法参数量多、计算量大的问题,提出一种结合知识蒸馏(KD)的轻量级红外船舶目标检测算法CB-YOLOv5。在YOLOv5s主干网络引入轻量级网络Ghost模型以实现检测主干网络的轻量化;引用新的颈部结构渐进型特征金字塔网络(AFPN),AFPN通过融合两个相邻的低级特征,并渐进向高级特征融合,避免了非相邻级别较大的语义差距;使用VFL函数,改善红外船舶目标检测任务中正负样本不平衡的问题,以提升模型的整体性能;结合KD算法将学习能力强的教师网络中的“知识”迁移到所改进的网络模型中,来提升分类和定位的准确率。实验结果表明,在红外船舶数据集中,与原算法YOLOv5s相比,参数量减少了38%,mAP提升了3.9个百分点,模型权重文件大小仅为8.96×10^(6),证明了所提算法的有效性,具有一定的实用价值。

关键词：目标检测 YOLOv5s 轻量化知识蒸馏

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基于改进YOLOv4的电力设备红外图像识别

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激光与光电子学进展 2022年第24期59卷 21-28页

作者：段中兴张雨明马佳豪西安建筑科技大学信息与控制工程学院陕西西安710055 国网西安供电公司陕西西安710032

针对电力设备红外图像存在的图像视觉效果差、尺度差异性大、数据类别不平衡等问题,提出了一种基于改进YOLOv4的目标检测模型。利用该模型并结合MSRCR图像增强算法对电力设备红外图像进行识别。首先,构建电力设备红外图像目标检测数据集... 详细信息

针对电力设备红外图像存在的图像视觉效果差、尺度差异性大、数据类别不平衡等问题,提出了一种基于改进YOLOv4的目标检测模型。利用该模型并结合MSRCR图像增强算法对电力设备红外图像进行识别。首先,构建电力设备红外图像目标检测数据集,使用MSRCR算法对原始红外图像进行增强处理,改善雨雾天气下红外图像存在的对比度低、像素模糊等问题,进而提高模型在雨雾天气下对电力设备的检测能力;其次,在YOLOv4骨干网络中引入多尺度卷积模块,通过不同大小的卷积核获取输入图像的多尺度特征,增强对初始特征的表征能力;为进一步提升检测精度,采用Focal loss函数,解决了红外图像数据不均衡导致的分类困难问题。经测试,所提方法对8类电力设备的平均识别精度达96.31%,检测速度达71 frame/s。实验结果验证了所提方法的有效性。

关键词：目标检测图像增强红外图像电力设备识别

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Band selection for heterogeneity classification of hyperspectral transmission images based on multi-criteria ranking

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infrared PHYSICS & technology 2022年第0期125卷

作者： Li, Gang Ma, Shuangshuang Li, Ke Zhou, Mei Lin, Ling Tianjin Univ State Key Lab Precis Measuring Technol & Instrumen Tianjin 300072 Peoples R China Tianjin Univ Tianjin Key Lab Biomed Detecting Tech & Instrument Tianjin 300072 Peoples R China Tianjin Univ Hosp Dept Obstet & Gynecol Tianjin 300072 Peoples R China East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai 200241 Peoples R China

Hyperspectral images contain hundreds of continuous spectral bands, which can provide rich information for target detection and image classification. However, high-dimensional data are easy to cause the "Hughes phenomenon", which affects the classification accuracy of images. In this paper, a band selection method based on multi-criteria ranking is proposed for hyperspectral dimensionality reduction. First, the hyperspectral image acquisition system is built to collect the hyperspectral transmission image datasets containing various heterogeneities. Second, the original data are preprocessed and the band selection method proposed is used for data dimensionality reduction. That is, (1) the whole band is divided into subintervals according to the correlation criteria, and then the bands with large information amount and good inter-class separability are selected according to other criteria functions, (2) the bands of each subinterval are combined, and the combined bands are evaluated according to the optimum index factor and Bhattacharyya distance, and (3) the combined bands are ranked and the top-ranked combined bands are selected as the representative bands for image classification. Finally, the support vector machine is used to verify the effectiveness of the representative bands selected by this method. The experimental results show that the proposed method achieves data dimensionality reduction, and the overall classification accuracy can reach 100% on some selected subsets of bands, which has better classification performance than other related band selection methods. In addition, the computational complexity of high-dimensional data can be effectively reduced by band selection, and the average running time is 95.19% less than that of the full bands while ensuring classification accuracy.

关键词： Band selection Multi -criteria ranking method Heterogeneity classification Hyperspectral transmission images

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infrared Image target detection Algorithm for Hardware Fittings at Tension Clamp of Qishao UHV Transmission Line

Infrared Image Target Detection Algorithm for Hardware Fitti...

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International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)

作者： Wei Zhang Weiguang Dong Shengchang Li School of Automation and Electrical Engineering Lanzhou Jiaotong University Lanzhou China

The infrared thermal imager is a common method for electric inspection to detect the abnormal heat of the gold tool at the tensioning wire clamp. Because the Gansu section of Qishao High voltage line accounts for about 52.4% of the total length, through a lot of complex geographical environment, even once a quarter manual inspection will cost time and effort, subjectivity is big, and real-time performance is poor. Aiming at the above, this paper proposes an improved YOLOv4 multi-target recognition algorithm for infrared images. Firstly, the backbone feature extraction network CSPDaeknet53 is replaced by Mobilenetv1. Secondly, the training dataset is increased by the base hjs image expansion technology. Finally, Mosaic data enhancement and cosine annealing attenuation technology are introduced to construct an effective infrared image detection model to realize the classification and recognition of the hardware. The experimental results are compared with SSD, YOLOv3 and YOLOv4 target detection algorithms, and it is found that the average recognition accuracy of the proposed method is 93.84%, and the model size is only 55.53MB. Under the condition of ensuring accuracy, the model is smaller and has practical reference value.

关键词： Fault diagnosis Training Image recognition Object detection High-voltage techniques Inspection Inverters

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infrared Image Recognition technology Based on Visual Processing and Deep Learning

Infrared Image Recognition Technology Based on Visual Proces...

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Chinese Automation Congress (CAC)

作者： He Feng Hu Xuran Liu Bin Wang Haipeng Zhang Decai Shandong Elect Power Co Jinan Power Supply Co Jinan 250101 Peoples R China State Grid Intelligence Technol Co Ltd Jinan 250101 Peoples R China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176871

infrared image recognition in substation is always a difficult problem. In order to solve the problem of recognition of knife gate, insulator and other components in infrared image, the target location technology of deep learning is proposed to realize the detection and recognition of typical components in infrared image, and the multi-target detection algorithm YOLO is selected to locate and identify the defects. Firstly, the image preprocessing technology is used to process the collected image, so as to filter the interference of background and other factors on the equipment identification. Then, the infrared image is detected by the YOLO target detection model based on multi feature fusion, so as to locate the position of inspection equipment in the infrared image. Then, the type of equipment is identified by the trained equipment classification model. Finally, the algorithm is tested with a large number of pictures in the substation scene.

关键词： infrared image recognition substation scene classification model

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Evaluation of chemometric classification and regression models for the detection of syrup adulteration in honey

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LWT-FOOD SCIENCE AND technology 2022年 163卷

作者： Boateng, Andrews A. Sumaila, Salma Lartey, Michael Oppong, Mahmood B. Opuni, Kwabena F. M. Adutwum, Lawrence A. Univ Ghana Sch Pharm Dept Pharmaceut Chem POB LG 43legon Accra Ghana Univ Alberta Dept Chem Edmonton AB T6G 2G2 Canada

Honey is a highly desirable commodity hence a target of adulteration to increase its bulk. Spectroscopic methods and chemometrics offer a fast, easy, and simple approach to the detection of syrup adulterations in honey. The idiosyncrasies associated with different instrumental data suggest varying performance for each algorithm. The objective of this study was to evaluate the performance of five commonly used classification and regression algorithms for the detection of syrup adulteration in honey. The prediction performance of the classification and regression algorithms was evaluated using data obtained from Fourier-transform infrared spectrometer with a Horizontal Attenuated Total Reflectance (HATR) Accessory analysis of honey samples. Gradient boosted discriminant analysis (GBDA) and Support vector machines discriminant analysis (SVMDA) were able to differentiate between adulterated and pure honey samples with an external validation set prediction accuracies of 0.988 and 0.981, respectively. Application of feature selection method did not lead to improved prediction accuracies. Gradient boosted regression (GBR) initialized with a ridge regression predicted the percentage level of adulteration with a regression coefficient of 1.000. The RMSE for the optimal GBR was 2.183 and 0.018 for before and after feature selection with the partial least squares (PLS) algorithm, respectively. Ensemble methods are generally better for both classification and regression of honey using FTIR-HATR spectroscopic data.

关键词： Chemometrics Machine learning Honey adulteration Feature selection FTIR

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基于支持向量机的泄漏气体云团热成像检测方法

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光学学报 2022年第9期42卷 96-103页

作者：翁静袁盼王铭赫李力金伟其曹伟孙秉才北京理工大学光电成像技术与系统教育部重点实验室北京100081 北京智慧共享技术服务有限公司北京100125 中国石油集团安全环保技术研究院有限公司北京102206

基于热成像的气体泄漏检测技术以其检测效率高、直观可视等优点,已成为石油天然气泄漏检测的重要手段,但常规的气体泄漏热成像检测方法需要检测人员从视频图像中主观地判断泄漏气体痕迹,容易发生漏检、误检。研究了一种基于尺度不变特... 详细信息

基于热成像的气体泄漏检测技术以其检测效率高、直观可视等优点,已成为石油天然气泄漏检测的重要手段,但常规的气体泄漏热成像检测方法需要检测人员从视频图像中主观地判断泄漏气体痕迹,容易发生漏检、误检。研究了一种基于尺度不变特征变换(SIFT)和支持向量机(SVM)的泄漏气体云团热成像检测算法,采用帧间差分法从红外图像序列中筛选目标区域;分别提取泄漏气体和干扰物的SIFT特征;使用SVM对候选区域进行目标判别,提取泄漏气体云团目标。针对真实复杂场景中包含乙烯、甲烷等的气体泄漏图像和运动人员、漂动树木、野草等干扰图像,建立了1000个典型目标图像数据库,通过图像检测仿真,可得所提算法对距10~150 m处的泄漏气体云团的分类准确率可达92.5%。结果表明,采用该检测方法可自动排除其他运动物体的干扰,有效检测出泄漏气体云团。

关键词：成像系统热成像气体泄漏检测气体云团尺度不变特征变换支持向量机

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基于多维特征的高超声速目标分类识别算法

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战术导弹技术 2023年第2期 81-87页

作者：高羽婷李享孙鹤枝尹可李超李翰夫杨冬李劲东中国空间技术研究院北京100094 哈尔滨工业大学哈尔滨150006

针对高超声速目标天基探测中分类识别困难的问题,提出了一种基于多维红外特征的高超声速目标分类识别算法。在对主动巡航式和被动滑翔式两类不同机动类型的典型高超声速目标多维红外特征进行对比研究的基础上,结合高斯混合模型进行机器... 详细信息

针对高超声速目标天基探测中分类识别困难的问题,提出了一种基于多维红外特征的高超声速目标分类识别算法。在对主动巡航式和被动滑翔式两类不同机动类型的典型高超声速目标多维红外特征进行对比研究的基础上,结合高斯混合模型进行机器学习聚类分析,开发了基于多维特征的高超声速目标分类识别算法,构建了高超声速目标多维特征混合识别模型。基于天基探测全链路模型仿真的数据进行测试验证,其结果证明了所提出的高超声速目标分类识别方法的有效性。在检测到的高超声速目标运动特性未知的情况下,该算法仅通过多维红外特征可快速对两型高超声速目标(主动巡航式/被动滑翔式)进行分类识别,为后续跟踪和拦截高超声速目标争取了时间。

关键词：高超声速目标天基探测多维特征目标分类识别聚类算法高斯混合模型

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