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6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Guo, Yonghui CCTEG Taiyuan Research Institute Co. Ltd China Shanxi Tiandi Coal Mining Machinery Co. Ltd China China National Engineering Laboratory for Coal Mining Machinery China

ISBN: (纸本)9798350316520

In coal mines, infrared thermal imaging technology can be used to monitor and identify underground personnel to help ensure the safety of miners. Through infrared thermal imaging technology, the body temperature distribution of miners can be monitored in real time, and abnormal conditions can be found in time, such as abnormally high or abnormally low body temperature, so that timely measures can be taken to ensure the safety of miners. In this paper, the methods of median filter, sobel operator image segmentation, HOG feature extraction and SVM classification and recognition are used to process the human walking image in coal mine underground roadway, and the test results can meet the needs of human detection and recognition in coal mine. © 2024 IEEE.

关键词： Support vector machines

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Integrating hyperspectrograms with class modeling techniques for the construction of an effective expert system: Quality control of pharmaceutical tablets based on near-infrared hyperspectral imaging

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JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS 2025年 256卷 116697页

作者： Pieszczek, Lukasz Daszykowski, Michal Univ Silesia Katowice Inst Chem 9 Szkolna St PL-40006 Katowice Poland Univ Silesia Katowice Ctr Microscop Studies Matter SPIN Lab 75 Pulku Piechoty St 1 PL-41500 Chorzow Poland

Near-infrared hyperspectral imaging (NIR-HSI) integrated with expert systems can support the monitoring of active pharmaceutical ingredients (APIs) and provide effective quality control of tablet formulations. However, existing quality control methods usually test a limited number of variability sources affecting the final product. This study examines the potential of NIR-HSI (in the spectral range of 935.61-1720.2 nm) as an advanced and high-throughput detector to identify different manufacturing factors and their fluctuations that impact tablet properties. These are, for instance, particle sizes of powdered excipients, their mixing, compression force used to form a tablet, origin of ingredients, storage conditions, and concentration of API. During the study, the novel expert system approach was developed to support NIR-HSI, enabling the detection of subtle, diverse substandard anomalies in tablets. The system combines (i) hyperspectrograms, which characterize and simplify tablet spatial heterogeneity through principal component analysis scores distribution, and (ii) a one-class classifier (OCC), trained exclusively on target class samples, without the need for substandard tablets. The system was trained to recognize known sources of variation and validated using tablets with cellulose, magnesium stearate, and ascorbic acid as API. It outperformed the alternative approach based on averaged spectra, achieving 100.00% sensitivity and 98.77% specificity.

关键词： Near-infrared hyperspectral imaging (NIR-HSI) Process analytical technology (PAT) Pharmaceutical quality control Hyperspectrogram One-class classification

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ASSK-Net: Automatic Spatial Selection Kernel Network for Faster Oriented Object detection

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2025年 1-14页

作者： Hu, Yong Jing, Zhengbiao Su, Yun Zhang, Fang Jing, Donglin Chengdu Vocat & Tech Coll Ind Sch Informat Engn Chengdu Peoples R China Chengdu Vocat & Tech Coll Ind Sch Intelligent Mfg & Automot Chengdu Peoples R China Shanghai Aerosp Control Technol Inst Shanghai 201109 Peoples R China China Aerosp Sci & Technol Corp Res & Dev Ctr Infrared Detect Technol Shanghai 201109 Peoples R China

The improvement in existing remote sensing detectors often relies on complex structures and deeper convolutional layers, leading to a significant increase in computational complexity. While previous works have designed various novel lightweight convolutions to improve target feature representation, these structures often overlook the unique prior knowledge of remote sensing scenes when applied to aerial detection tasks, resulting in decreased detection performance. Specifically, this prior knowledge includes: (1) Accurate detection of aerial objects often requires extensive contextual information. (2) The range of contextual information needed for different categories varies greatly. To address this, our paper fully considers this prior knowledge and proposes the Adaptive Spatial Selection Kernel Network (ASSK-Net). We first use large kernel convolution decomposition operations to decouple a series of convolutions with smaller sizes and rapidly expanding dilation rates, constructing diverse receptive fields with different spatial coverage ranges while significantly reducing computation. Based on this, a spatial selection mechanism is used to effectively weight feature maps with different levels of information richness, enhancing the feature differences between targets and backgrounds. Finally, we design an adaptive class equilibrium loss function, increasing the loss weight with rare categories and other easily confused categories with similar classification scores, thus improving the discriminative capability for rare categories. Comprehensive experiments on datasets (such as DOTA, UCAS-AOD, and HRSC2016) indicate that our ASSK-Net achieves optimal performance, effectively balancing computational complexity and detection accuracy.

关键词： Lightweight network Oriented object detection Convolution decomposition Sample balance

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Deep Learning-Based Algorithm for Local Feature Recognition of target infrared Polarization 3

Deep Learning-Based Algorithm for Local Feature Recognition ...

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3rd International Conference on Optics and Image Processing, ICOIP 2023

作者： Zou, Zhihui Yang, Shuai Li, Yingchao Shi, Haodong Fu, Qiang School of Optoelectronic Engineering Changchun University of Science and Technology Jilin Changchun China Jilin Provincial Key Laboratory of Space Optoelectronics Technology Jilin Changchun China

ISBN: (纸本)9781510667426

In response to the traditional detection methods of not detecting the target, not seeing it, blurring the outline and details of the image, etc., using a combination of infrared and polarization detection, the infrared image information and the polarization image information are decoded to solve the problem of not being detected or seen in various environments. For the target local feature extraction process of large amounts of data, slow extraction speed, and other problems, an improved SIFT algorithm for local feature extraction of polarized images with deep learning is proposed. Experimental results show that the modified algorithm combines the advantages of polarimetric imaging and deep learning to achieve fast feature extraction of targets in simple or complex backgrounds. The algorithm improves the speed of local feature extraction of polarized images by 3.6% and the accuracy of extraction by 2.8%. The algorithm provides the theoretical basis for target classification, identification, and tracking techniques. © 2023 SPIE. All rights reserved.

关键词： Feature extraction

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Fuzzy recognition of missile borne multi-line array infrared detection based on size calculating

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Defence technology（防务技术） 2021年第4期17卷 1135-1142页

作者： Bing-shan Lei Jing Li Wei-na Hao Ke-ding Yan School of Mechanical and Electrical Engineering Xi’an Technological UniversityXi’an710021China School of Electronic and Information Engineering Xi’an Technological UniversityXi’an710021China Key Laboratory of Education Ministry for Modern Design&Rotor-Bearing System Xi’an Jiaotong UniversityXi’an710049China

In order to improve the infrared detection and discrimination ability of the smart munition to the dynamic armor target under the complex background,the multi-line array infrared detection system is established based on the combination of the single unit infrared *** surface dimension features of ground armored targets are identified by size calculating solution *** signal response value and the value of size calculating are identified by the method of fuzzy recognition to make the fuzzy classification judgment for armored *** to the characteristics of the target signal,a custom threshold de-noising function is proposed to solve the problem of signal *** multi-line array infrared detection can complete the scanning detection in a large area in a short time with the characteristics of smart munition in the steady-state scanning *** method solves the disadvantages of wide scanning interval and low detection probability of single unit infrared *** reducing the scanning interval,the number of random rendezvous in the infrared feature area of the upper surface is increased,the accuracy of the size calculating is *** experiments results show that in the fuzzy recognition method,the size calculating is introduced as the feature operator,which can improve the recognition ability of the ground armor target with different shape size.

关键词： Multi-line array infrared detection Size calculating Custom threshold de-noising Fuzzy comprehensive discrimination algorithm

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Heterogeneous face detection based on multi-task cascaded convolutional neural network

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IET IMAGE PROCESSING 2022年第1期16卷 207-215页

作者： Yang, XianBen Zhang, Wei Beihua Univ Coll Comp Sci & Technol Jilin 13200 Jilin Peoples R China

Facial target detection is an important task in computer vision. Because heterogeneous face detection shows broad prospects, it has attracted extensive attention from the academic community. In recent years, with the rise of deep learning and its applications in computer vision, face detection technology has made great strides. This paper uses multi-task cascaded convolutional neural network (MTCNN) for heterogeneous face feature detection. This algorithm makes full use of the advantages of image pyramid, boundary regression, fully convolutional attention networks and non-maximum suppression. The main idea of this paper is to use candidate frame plus classifier for fast and efficient face detection. Specifically, the candidate window is generated by the proposal network (P-Net), and the high-precision candidate window is filtered and selected by the reduced network (R-Net), and the final bounding box and facial key points are generated by the output network (O-Net). In order to prove the effectiveness of this method in visible light, near-infrared and sketch face recognition scenes, it was verified in the datasets of CUFS, CUFSF and CASIA NIR-VIS 2.0. Experiments show that this method is effective for face images in heterogeneous face and is better than the latest algorithms.

关键词： O-Net regression analysis fully convolutional attention networks P-Net reduced network CASIA NIR-VIS 2.0 heterogeneous face feature detection candidate window frame plus classifier convolutional neural nets deep learning R-Net image representation output network face images feature extraction heterogeneous face detection deep learning (artificial intelligence) face recognition MTCNN multi-task cascaded convolutional neural network CUFSF computer vision boundary regression CUFS near-infrared sketch face recognition scenes object detection face detection technology nonmaximum suppression image classification image pyramid facial target detection

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infrared dim target detection via hand-crafted features and deep information combination 17

Infrared dim target detection via hand-crafted features and ...

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17th Conference on Industrial Electronics and Applications (ICIEA)

作者： Cao, Siying Lian, Ruiyi Zhang, Yiwei Wu, Fengyi Peng, Zhenming Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China

ISBN: (纸本)9781665409841

infrared target detection is the key technology of infrared search and tracking system. In the case of complex background, dim target detection face difficulties such as high algorithm complexity and low robustness. To solve these problems, an infrared dim target detection method via handcrafted features and deep information combination is proposed. Firstly, an adaptive Top-Hat segmentation is used to extract candidate target region, then 128-dimensional deep learning features and 106-dimensional hand-crafted features are calculated for candidate region respectively. Finally, features are concatenated and input into a 4-layer fully connected network for feature fusion and classification to exclude false alarm sources. Experiments show that the proposed method can detect infrared dim targets accurately and robustly in various scenes.

关键词： infrared target detection dim target feature fusion

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High anti-interference and FPGA-oriented method for real-time ship detection based on structured LBP features

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JOURNAL OF SUPERCOMPUTING 2022年第11期78卷 13780-13813页

作者： Miao, Rui Jiang, Hongxu Tian, Fangzheng Li, Xiaobin Beihang Univ Sch Comp Sci & Engn Beijing Key Lab Digital Media Beijing 100191 Peoples R China

With the continuous enhancement of remote sensing technology, using satellite to detect and identify targets has important research significance in military and civil fields. Due to the influence of many interference factors, the background of large-scale infrared remote sensing images is often complex and the target intensity is weak, and efficient and accurate target detection has become a difficult issue. Therefore, the purpose of this paper is to solve the issues of inaccurate target detection and inefficient hardware deployment. Firstly, the rough detection method based on the grey variance and gradient features can effectively extracted the target candidate region and eliminate simple background interference. Secondly, the fine detection method based on multiple features is used for ship classification, in which structured LBP (local binary pattern) features is applied to calculate the structured feature histogram, screen the local target features, and calculate and classify the global features through two-dimensional Fourier features. Thirdly, the improved non-maximum suppression method is used to merge adjacent target points to ensure the accuracy of the algorithm to the greatest extent. Finally, an efficient feature computing unit and multichannel storage structure is proposed to optimize the FPGA (field-programmable gate array) implementation and relieve the pressure of high data rate processing and can eliminate a lot of computational redundancy. Experimental results validate that, compared with some advanced methods, our method can reduce the processing time by up to 1.71 times while the ship detection performance can be improved by up to 5.4%. Furthermore, the hardware processing capability was verified by three indicators, among which the traversal processing performance and throughput can increase by up to 1.7 and 6.7%, while the delays of key modules can decrease by up to 4%.

关键词： Ship detection Structured LBP features Anti-interference FPGA-oriented Real time

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infrared aircraft few-shot classification method based on cross-correlation network

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红外与毫米波学报 2025年第1期44卷 103-111页

作者： HUANG Zhen ZHANG Yong GONG Jin-Fu Key Laboratory of Infrared System Detection and Imaging Technology Chinese Academy of SciencesShanghai 200083China University of Chinese Academy of Sciences Beijing 100049China Shanghai Institute of Technical Physics Chinese Academy of SciencesShanghai 200083China

In response to the scarcity of infrared aircraft samples and the tendency of traditional deep learning to overfit,a few-shot infrared aircraft classification method based on cross-correlation networks is *** method combines two core modules:a simple parameter-free self-attention and *** analyzing the self-correlation and cross-correlation between support images and query images,it achieves effective classification of infrared aircraft under few-shot *** proposed cross-correlation network integrates these two modules and is trained in an end-to-end *** simple parameter-free self-attention is responsible for extracting the internal structure of the image while the cross-attention can calculate the cross-correlation between images further extracting and fusing the features between *** with existing few-shot infrared target classification models,this model focuses on the geometric structure and thermal texture information of infrared images by modeling the semantic relevance between the features of the support set and query set,thus better attending to the target *** results show that this method outperforms existing infrared aircraft classification methods in various classification tasks,with the highest classification accuracy improvement exceeding 3%.In addition,ablation experiments and comparative experiments also prove the effectiveness of the method.

关键词： infrared imaging aircraft classification few-shot learning parameter-free attention cross attention

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One-shot gas detection with transformer paired neural networks in Mako collected longwave infrared hyperspectral imagery 30

One-shot gas detection with transformer paired neural networ...

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Artificial Intelligence and Image and Signal Processing for Remote Sensing XXX 2024

作者： Benham, Kevin Deneke, Elihu Sandia National Laboratories 1515 Eubank Blvd. AlbuquerqueNM87123 United States

ISBN: (纸本)9781510681002

To date, careful data treatment workflows and statistical detectors are used to perform hyperspectral image (HSI) detection of any gas contained in a spectral library which is often expanded with physics-models to incorporate different spectral characteristics. Generally, surrounding evidence or known gas-release parameters are used to provide confidence in or confirm detection capability, respectively. This makes quantifying detection performance difficult as it is nearly impossible to develop absolute ground truth for gas target pixel presence in collected HSI. Consequently, developing and comparing new detection methods, especially machine learning (ML) based methods, is beholden to subjectivity in derived detection map quality. In this work, we demonstrate the first use of transformer-based paired neural networks (PNNs) for one-shot gas target detection for multiple gases while providing quantitative classification and detection metrics for their use on labeled data. Terabytes of training data are generated from a database of longwave infrared (LWIR) HSI obtained from historical Mako sensor campaigns over Los Angles. By incorporating labels, singular signature representations, and a model development pipeline, we can tune & select PNNs to detect multiple gas targets which are not seen in training on a quantitative basis. We additionally assess our test set detections using interpretability techniques widely employed for ML-based predictors, but less common on detection methods relying on learned latent spaces. © 2024 SPIE.

关键词： Hyperspectral imaging

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